rvenkat + teaching   575

How Much Mightier Is the Pen than the Keyboard for Note-Taking? A Replication and Extension of Mueller and Oppenheimer (2014) | SpringerLink
Many students use laptops to take notes in classes, but does using them impact later test performance? In a high-profile investigation comparing note-taking writing on paper versus typing on a laptop keyboard, Mueller and Oppenheimer (Psychological Science, 25, 1159–1168, 2014) concluded that taking notes by longhand is superior. We conducted a direct replication of Mueller and Oppenheimer (2014) and extended their work by including groups who took notes using eWriters and who did not take notes. Some trends suggested longhand superiority; however, performance did not consistently differ between any groups (experiments 1 and 2), including a group who did not take notes (experiment 2). Group differences were further decreased after students studied their notes (experiment 2). A meta-analysis (combining direct replications) of test performance revealed small (nonsignificant) effects favoring longhand. Based on the present outcomes and other available evidence, concluding which method is superior for improving the functions of note-taking seems premature.

pedagogy  teaching 
6 weeks ago by rvenkat
[1711.09504] A Typology of Social Capital and Associated Network Measures
I provide a typology of social capital, breaking it down into seven more fundamental forms of capital: information capital, brokerage capital, coordination and leadership capital, bridging capital, favor capital, reputation capital, and community capital. I discuss how most of these forms of social capital can be identified using different network-based measures.
matthew.jackson  social_networks  networks  teaching 
8 weeks ago by rvenkat
[1808.09004] Downstream Effects of Affirmative Action
We study a two-stage model, in which students are 1) admitted to college on the basis of an entrance exam which is a noisy signal about their qualifications (type), and then 2) those students who were admitted to college can be hired by an employer as a function of their college grades, which are an independently drawn noisy signal of their type. Students are drawn from one of two populations, which might have different type distributions. We assume that the employer at the end of the pipeline is rational, in the sense that it computes a posterior distribution on student type conditional on all information that it has available (college admissions, grades, and group membership), and makes a decision based on posterior expectation. We then study what kinds of fairness goals can be achieved by the college by setting its admissions rule and grading policy. For example, the college might have the goal of guaranteeing equal opportunity across populations: that the probability of passing through the pipeline and being hired by the employer should be independent of group membership, conditioned on type. Alternately, the college might have the goal of incentivizing the employer to have a group blind hiring rule. We show that both goals can be achieved when the college does not report grades. On the other hand, we show that under reasonable conditions, these goals are impossible to achieve even in isolation when the college uses an (even minimally) informative grading policy.

--would make a nice example in a game theory class.
decison_theory  game_theory  algorithmic_fairness  discrimination  affirmative_action  economics  computer_science  teaching 
8 weeks ago by rvenkat
[1801.07351] Tracking network dynamics: a survey of distances and similarity metrics
From longitudinal biomedical studies to social networks, graphs have emerged as a powerful framework for describing evolving interactions between agents in complex systems. In such studies, after pre-processing, the data can be represented by a set of graphs, each representing a system's state at different points in time. The analysis of the system's dynamics depends on the selection of the appropriate analytical tools. After characterizing similarities between states, a critical step lies in the choice of a distance between graphs capable of reflecting such similarities. While the literature offers a number of distances that one could a priori choose from, their properties have been little investigated and no guidelines regarding the choice of such a distance have yet been provided. In particular, most graph distances consider that the nodes are exchangeable and do not take into account node identities. Accounting for the alignment of the graphs enables us to enhance these distances' sensitivity to perturbations in the network and detect important changes in graph dynamics. Thus the selection of an adequate metric is a decisive --yet delicate--practical matter.
In the spirit of Goldenberg, Zheng and Fienberg's seminal 2009 review, the purpose of this article is to provide an overview of commonly-used graph distances and an explicit characterization of the structural changes that they are best able to capture. We use as a guiding thread to our discussion the application of these distances to the analysis of both a longitudinal microbiome dataset and a brain fMRI study. We show examples of using permutation tests to detect the effect of covariates on the graphs' variability. Synthetic examples provide intuition as to the qualities and drawbacks of the different distances. Above all, we provide some guidance for choosing one distance over another in certain types of applications.
temporal_networks  review  network_data_analysis  teaching  ?  for_friends 
10 weeks ago by rvenkat
Structure and dynamical behavior of non-normal networks | Science Advances
We analyze a collection of empirical networks in a wide spectrum of disciplines and show that strong non-normality is ubiquitous in network science. Dynamical processes evolving on non-normal networks exhibit a peculiar behavior, as initial small disturbances may undergo a transient phase and be strongly amplified in linearly stable systems. In addition, eigenvalues may become extremely sensible to noise and have a diminished physical meaning. We identify structural properties of networks that are associated with non-normality and propose simple models to generate networks with a tunable level of non-normality. We also show the potential use of a variety of metrics capturing different aspects of non-normality and propose their potential use in the context of the stability of complex ecosystems.

Also here
networks  linear_algebra  dynamical_system  network_data_analysis  teaching  ? 
10 weeks ago by rvenkat
Interpreting economic complexity | Science Advances
Two network measures known as the economic complexity index (ECI) and product complexity index (PCI) have provided important insights into patterns of economic development. We show that the ECI and PCI are equivalent to a spectral clustering algorithm that partitions a similarity graph into two parts. The measures are also closely related to various dimensionality reduction methods, such as diffusion maps and correspondence analysis. Our results shed new light on the ECI’s empirical success in explaining cross-country differences in gross domestic product per capita and economic growth, which is often linked to the diversity of country export baskets. In fact, countries with high (low) ECI tend to specialize in high-PCI (low-PCI) products. We also find that the ECI and PCI uncover specialization patterns across U.S. states and U.K. regions.
networks  economic_geography  teaching 
10 weeks ago by rvenkat
Misinformation and Conspiracy Theories about Politics and Public Policy
Why do people hold false or unsupported beliefs about politics and public policy and why are so those beliefs so hard to change? This three-credit graduate course will explore the psychological factors that make people vulnerable to misinformation and conspiracy theories and the reasons that corrections so often fail to change their minds. We will also analyze how those tendencies are exploited by political elites and consider possible approaches that journalists, civic reformers, and government officials could employ to combat misperceptions. Students will develop substantive expertise in how to measure, diagnose, and respond to false beliefs about politics and public policy; methodological expertise in reading and analyzing quantitative and experimental research in social science; and analytical writing skills in preparing a final research paper applying one or more theories from the course to help explain the development and spread of a specific misperception or conspiracy theory.
brendan.nyhan  course  misinformation  disinformation  public_opinion  public_policy  conspiracy_theories  political_science  teaching  dmce  networks 
11 weeks ago by rvenkat
The Loss of Loss Aversion: Will It Loom Larger Than Its Gain? - Gal - 2018 - Journal of Consumer Psychology - Wiley Online Library
Loss aversion, the principle that losses loom larger than gains, is among the most widely accepted ideas in the social sciences. The first part of this article introduces and discusses the construct of loss aversion. The second part of this article reviews evidence in support of loss aversion. The upshot of this review is that current evidence does not support that losses, on balance, tend to be any more impactful than gains. The third part of this article aims to address the question of why acceptance of loss aversion as a general principle remains pervasive and persistent among social scientists, including consumer psychologists, despite evidence to the contrary. This analysis aims to connect the persistence of a belief in loss aversion to more general ideas about belief acceptance and persistence in science. The final part of the article discusses how a more contextualized perspective of the relative impact of losses versus gains can open new areas of inquiry that are squarely in the domain of consumer psychology.

--See additional references here

--First, hot-hand-fallacy fallacy; then, loss of loss aversion; now what? Time to rework my syllabus (Wonder what's the status of conjunction fallacy?)

-- Also, someone should inform this field of *quantum cognition*

of these developments.
judgment_decision-making  dmce  teaching  via:gelman 
december 2018 by rvenkat
A state variable for crumpled thin sheets | Communications Physics
Despite the apparent ease with which sheets of paper are crumpled and tossed away, crumpling dynamics are often considered a paradigm of complexity. This arises from the infinite number of configurations that disordered, crumpled sheets can take. Here we experimentally show that key aspects of axially confined crumpled Mylar sheets have a very simple description; evolution of damage in crumpling dynamics can largely be described by a single global quantity—the total length of creases. We follow the evolution of the damage network in repetitively crumpled elastoplastic sheets, and show that the dynamics are deterministic, depending only on the instantaneous state of the crease network and not on the crumpling history. We also show that this global quantity captures the crumpling dynamics of a sheet crumpled for the first time. This leads to a remarkable reduction in complexity, allowing a description of a highly disordered system by a single state parameter.

-- I know what I'll do when someone throws *it's must be a complex systems* argument at me.
physics  fun  complexity  ?  teaching  philosophy_of_science 
december 2018 by rvenkat
Social Influence Bias: A Randomized Experiment | Science
Our society is increasingly relying on the digitized, aggregated opinions of others to make decisions. We therefore designed and analyzed a large-scale randomized experiment on a social news aggregation Web site to investigate whether knowledge of such aggregates distorts decision-making. Prior ratings created significant bias in individual rating behavior, and positive and negative social influences created asymmetric herding effects. Whereas negative social influence inspired users to correct manipulated ratings, positive social influence increased the likelihood of positive ratings by 32% and created accumulating positive herding that increased final ratings by 25% on average. This positive herding was topic-dependent and affected by whether individuals were viewing the opinions of friends or enemies. A mixture of changing opinion and greater turnout under both manipulations together with a natural tendency to up-vote on the site combined to create the herding effects. Such findings will help interpret collective judgment accurately and avoid social influence bias in collective intelligence in the future.
crowd_sourcing  judgment_decision-making  social_influence  social_networks  teaching  online_experiments  sinan.aral 
november 2018 by rvenkat
OSF Preprints | Explanation, prediction, and causality: Three sides of the same coin?
In this essay we make four interrelated points. First, we reiterate previous arguments (Kleinberg et al 2015) that forecasting problems are more common in social science than is often appreciated. From this observation it follows that social scientists should care about predictive accuracy in addition to unbiased or consistent estimation of causal relationships. Second, we argue that social scientists should be interested in prediction even if they have no interest in forecasting per se. Whether they do so explicitly or not, that is, causal claims necessarily make predictions; thus it is both fair and arguably useful to hold them accountable for the accuracy of the predictions they make. Third, we argue that prediction, used in either of the above two senses, is a useful metric for quantifying progress. Important differences between social science explanations and machine learning algorithms notwithstanding, social scientists can still learn from approaches like the Common Task Framework (CTF) which have successfully driven progress in certain fields of AI over the past 30 years (Donoho, 2015). Finally, we anticipate that as the predictive performance of forecasting models and explanations alike receives more attention, it will become clear that it is subject to some upper limit which lies well below deterministic accuracy for many applications of interest (Martin et al 2016). Characterizing the properties of complex social systems that lead to higher or lower predictive limits therefore poses an interesting challenge for computational social science.
social_science  prediction  explanation  forecasting  causality  philosophy_of_science  duncan.watts  for_friends  teaching 
november 2018 by rvenkat
Norms in the Wild - Paperback - Cristina Bicchieri - Oxford University Press
The philosopher Cristina Bicchieri here develops her theory of social norms, most recently explained in her 2006 volume The Grammar of Society. Bicchieri challenges many of the fundamental assumptions of the social sciences. She argues that when it comes to human behavior, social scientists place too much stress on rational deliberation. In fact, many choices occur without much deliberation at all. Bicchieri's theory accounts for these automatic components of behavior, where individuals react automatically to cues--those cues often pointing to the social norms that govern our choices in a social world

Bicchieri's work has broad implications not only for understanding human behavior, but for changing it for better outcomes. People have a strong conditional preference for following social norms, but that also means manipulating those norms (and the underlying social expectations) can produce beneficial behavioral changes. Bicchieri's recent work with UNICEF has explored the applicability of her views to issues of human rights and well-being. Is it possible to change social expectations around forced marriage, genital mutilations, and public health practices like vaccinations and sanitation? If so, how? What tools might we use? This short book explores how social norms work, and how changing them--changing preferences, beliefs, and especially social expectations--can potentially improve lives all around the world.

--To-do: compare with Ullmann-Margalit's work. Don't remember the exact connections...
book  norms  dynamics  social_behavior  homophily  contagion  social_influence  networks  social_networks  teaching 
november 2018 by rvenkat
They saw a game; a case study
When the Dartmouth football team played Princeton in 1951, much controversy was generated over what actually took place during the game. Basically, there was disagreement between the two schools as to what had happened during the game. A questionnaire designed to get reactions to the game and to learn something of the climate of opinion was administered at each school and the same motion picture of the game was shown to a sample of undergraduate at each school, followed by another questionnnaire. Results indicate that the "game" was actually many different games and that each version of the events that transpired was just as "real" to a particular person as other versions were to other people.

groups  judgment_decision-making  collective_cognition  cultural_cognition  dmce  teaching  via:nyhan 
november 2018 by rvenkat
Kernighan, B.: Millions, Billions, Zillions: Defending Yourself in a World of Too Many Numbers (Hardcover and eBook) | Princeton University Press
Numbers are often intimidating, confusing, and even deliberately deceptive—especially when they are really big. The media loves to report on millions, billions, and trillions, but frequently makes basic mistakes or presents such numbers in misleading ways. And misunderstanding numbers can have serious consequences, since they can deceive us in many of our most important decisions, including how to vote, what to buy, and whether to make a financial investment. In this short, accessible, enlightening, and entertaining book, leading computer scientist Brian Kernighan teaches anyone—even diehard math-phobes—how to demystify the numbers that assault us every day.

With examples drawn from a rich variety of sources, including journalism, advertising, and politics, Kernighan demonstrates how numbers can mislead and misrepresent. In chapters covering big numbers, units, dimensions, and more, he lays bare everything from deceptive graphs to speciously precise numbers. And he shows how anyone—using a few basic ideas and lots of shortcuts—can easily learn to recognize common mistakes, determine whether numbers are credible, and make their own sensible estimates when needed.

Giving you the simple tools you need to avoid being fooled by dubious numbers, Millions, Billions, Zillions is an essential survival guide for a world drowning in big—and often bad—data.
book  numeracy  communication  education  via:zeynep  cognitive_science  mathematics  heuristics  dmce  teaching 
october 2018 by rvenkat
Ethics in statistical practice and communication: Five recommendations
Ethics in statistics is about more than good practice. It extends to the communication of uncertainty and variation. Andrew Gelman presents five recommendations for dealing with fundamental dilemmas
andrew.gelman  statistics  communication  ethics  machine_learning  for_friends  teaching 
october 2018 by rvenkat
[1810.03579] Long ties accelerate noisy threshold-based contagions
"Changes to network structure can substantially affect when and how widely new ideas, products, and conventions are adopted. In models of biological contagion, interventions that randomly rewire edges (making them "longer") accelerate spread. However, there are other models relevant to social contagion, such as those motivated by myopic best-response in games with strategic complements, in which individual's behavior is described by a threshold number of adopting neighbors above which adoption occurs (i.e., complex contagions). Recent work has argued that highly clustered, rather than random, networks facilitate spread of these complex contagions. Here we show that minor modifications of prior analyses, which make them more realistic, reverse this result. The modification is that we allow very rarely below threshold adoption, i.e., very rarely adoption occurs, where there is only one adopting neighbor. To model the trade-off between long and short edges we consider networks that are the union of cycle-power-k graphs and random graphs on n nodes. We study how the time to global spread changes as we replace the cycle edges with (random) long ties. Allowing adoptions below threshold to occur with order 1/n‾√ probability is enough to ensure that random rewiring accelerates spread. Simulations illustrate the robustness of these results to other commonly-posited models for noisy best-response behavior. We then examine empirical social networks, where we find that hypothetical interventions that (a) randomly rewire existing edges or (b) add random edges reduce time to spread compared with the original network or addition of "short", triad-closing edges, respectively. This substantially revises conclusions about how interventions change the spread of behavior, suggesting that those wanting to increase spread should induce formation of long ties, rather than triad-closing ties."
via:cshalizi  networks  contagion  teaching  for_friends 
october 2018 by rvenkat
[1809.08937] Networks and the Resilience and Fall of Empires: a Macro-Comparison of the Imperium Romanum and Imperial China
This paper proposes to proceed from a rather metaphorical application of network terminology on polities and imperial formations of the past to an actual use of tools and concepts of network science. For this purpose, a well established network model of the route system in the Roman Empire and a newly created network model of the infrastructural web of Imperial China are visualised and analysed with regard to their structural properties. Findings indicate that these systems could be understood as large scale complex networks with pronounced differences in centrality and connectivity among places and a hierarchical sequence of clusters across spatial scales from the overregional to the local level. Such properties in turn would influence the cohesion and robustness of imperial networks, as is demonstrated with two tests on vulnerability to node failure and to the collapse of longdistance connectivity. Tentatively, results can be connected with actual historical dynamics and thus hint at underlying network mechanisms of large scale integration and disintegration of political formations.
networks  history  spatial_statistics  network_data_analysis  geography  for_friends  teaching  via:noahpinion 
september 2018 by rvenkat
Godfrey-Smith, P.: Philosophy of Biology (Hardcover, Paperback and eBook) | Princeton University Press
This is a concise, comprehensive, and accessible introduction to the philosophy of biology written by a leading authority on the subject. Geared to philosophers, biologists, and students of both, the book provides sophisticated and innovative coverage of the central topics and many of the latest developments in the field. Emphasizing connections between biological theories and other areas of philosophy, and carefully explaining both philosophical and biological terms, Peter Godfrey-Smith discusses the relation between philosophy and science; examines the role of laws, mechanistic explanation, and idealized models in biological theories; describes evolution by natural selection; and assesses attempts to extend Darwin's mechanism to explain changes in ideas, culture, and other phenomena. Further topics include functions and teleology, individuality and organisms, species, the tree of life, and human nature. The book closes with detailed, cutting-edge treatments of the evolution of cooperation, of information in biology, and of the role of communication in living systems at all scales.

-- I have major issues with the last chapter of the book but is an adequate book. Also, the lack of chapters on cybernetics and absence of adequate discussions on self-organization and emergence. Also missing are literate discussions of non-equilibrium statistical mechanics and its role in the genesis of biological complexity. Overall, the book left me with the feeling of "where is the other half of the book?". But with a suitable collection of articles on missing topics, the book can serve as a text for a first course.
book  peter.godfrey-smith  philosophy_of_biology  teaching 
august 2018 by rvenkat
Stochastic Turing patterns in a synthetic bacterial population | PNAS
The origin of biological morphology and form is one of the deepest problems in science, underlying our understanding of development and the functioning of living systems. In 1952, Alan Turing showed that chemical morphogenesis could arise from a linear instability of a spatially uniform state, giving rise to periodic pattern formation in reaction–diffusion systems but only those with a rapidly diffusing inhibitor and a slowly diffusing activator. These conditions are disappointingly hard to achieve in nature, and the role of Turing instabilities in biological pattern formation has been called into question. Recently, the theory was extended to include noisy activator–inhibitor birth and death processes. Surprisingly, this stochastic Turing theory predicts the existence of patterns over a wide range of parameters, in particular with no severe requirement on the ratio of activator–inhibitor diffusion coefficients. To explore whether this mechanism is viable in practice, we have genetically engineered a synthetic bacterial population in which the signaling molecules form a stochastic activator–inhibitor system. The synthetic pattern-forming gene circuit destabilizes an initially homogenous lawn of genetically engineered bacteria, producing disordered patterns with tunable features on a spatial scale much larger than that of a single cell. Spatial correlations of the experimental patterns agree quantitatively with the signature predicted by theory. These results show that Turing-type pattern-forming mechanisms, if driven by stochasticity, can potentially underlie a broad range of biological patterns. These findings provide the groundwork for a unified picture of biological morphogenesis, arising from a combination of stochastic gene expression and dynamical instabilities.
pattern_formation  pde  dynamical_system  teaching  ecology  spatio-temporal_statistics 
august 2018 by rvenkat
[1408.6596] Emergence of Clustering in an Acquaintance Model without Homophily
We introduce an agent-based acquaintance model in which social links are created by processes in which there is no explicit homophily. In spite of the homogeneous nature of the social interactions, highly-clustered social networks can arise. The crucial feature of our model is that of variable transitive interactions. Namely, when an agent introduces two unconnected friends, the rate at which a connection actually occurs between them depends on the number of their mutual acquaintances. As this transitive interaction rate is varied, the social network undergoes a dramatic clustering transition. Close to the transition, the network consists of a collection of well-defined communities. As a function of time, the network can also undergo an \emph{incomplete} gelation transition, in which the gel, or giant cluster, does not constitute the entire network, even at infinite time. Some of the clustering properties of our model also arise, but in a more gradual manner, in Facebook networks. Finally, we discuss a more realistic variant of our original model in which there is a soft cutoff in the rate of transitive interactions. With this variant, one can construct network realizations that quantitatively match Facebook networks.
networks  teaching  sidney.redner 
july 2018 by rvenkat
Economic Consequences of Network Structure
We survey the literature on the economic consequences of the structure of social networks. We develop a taxonomy of "macro" and "micro" characteristics of social-interaction networks and discuss both the theoretical and empirical findings concerning the role of those characteristics in determining learning, diffusion, decisions, and resulting behaviors. We also discuss the challenges of accounting for the endogeneity of networks in assessing the relationship between the patterns of interactions and behaviors.
economics  social_networks  networks  review  matthew.jackson  teaching 
july 2018 by rvenkat
Non-assortative community structure in resting and task-evoked functional brain networks | bioRxiv
Brain networks exhibit community structure that reconfigures during cognitively demanding tasks. Extant work has emphasized a single class of communities: those that are assortative, or internally dense and externally sparse. Other classes that may play key functional roles in brain function have largely been ignored, leading to an impoverished view in the best case and a mischaracterization in the worst case. Here, we leverage weighted stochastic blockmodeling, a community detection method capable of detecting diverse classes of communities, to study the community structure of functional brain networks while subjects either rest or perform cognitively demanding tasks. We find evidence that the resting brain is largely assortative, although higher order association areas exhibit non-assortative organization, forming cores and peripheries. Surprisingly, this assortative structure breaks down during tasks and is supplanted by core, periphery, and disassortative communities. Using measures derived from the community structure, we show that it is possible to classify an individual's task state with an accuracy that is well above average. Finally, we show that inter-individual differences in the composition of assortative and non-assortative communities is correlated with subject performance on in-scanner cognitive tasks. These findings offer a new perspective on the community organization of functional brain networks and its relation to cognition.

-- for class discussions and/or student projects. Not sure about their implications... need to read more carefully.
networks  connectome  community_detection  via:clauset  teaching 
june 2018 by rvenkat
Complex Spreading Phenomena in Social Systems - Influence and Contagion in Real-World Social Networks | Sune Lehmann | Springer
This text is about spreading of information and influence in complex networks. Although previously considered similar and modeled in parallel approaches, there is now experimental evidence that epidemic and social spreading work in subtly different ways. While previously explored through modeling, there is currently an explosion of work on revealing the mechanisms underlying complex contagion based on big data and data-driven approaches.

This volume consists of four parts. Part 1 is an Introduction, providing an accessible summary of the state of the art. Part 2 provides an overview of the central theoretical developments in the field. Part 3 describes the empirical work on observing spreading processes in real-world networks. Finally, Part 4 goes into detail with recent and exciting new developments: dedicated studies designed to measure specific aspects of the spreading processes, often using randomized control trials to isolate the network effect from confounders, such as homophily.

Each contribution is authored by leading experts in the field. This volume, though based on technical selections of the most important results on complex spreading, remains quite accessible to the newly interested. The main benefit to the reader is that the topics are carefully structured to take the novice to the level of expert on the topic of social spreading processes. This book will be of great importance to a wide field: from researchers in physics, computer science, and sociology to professionals in public policy and public health.

networks  epidemics  contagion  social_networks  teaching  book 
june 2018 by rvenkat
Internal Colonialism, Core-Periphery Contrasts and Devolution: An Integrative Comment on JSTOR
The idea of internal colonialism is presented as a framework for examining regional deprivation, especially in distinct cultural environments, and is considered in the light of the devolution debate.
economics  political_science  networks  economic_geography  economic_sociology  teaching 
may 2018 by rvenkat
[1708.06401] A Tutorial on Hawkes Processes for Events in Social Media
This chapter provides an accessible introduction for point processes, and especially Hawkes processes, for modeling discrete, inter-dependent events over continuous time. We start by reviewing the definitions and the key concepts in point processes. We then introduce the Hawkes process, its event intensity function, as well as schemes for event simulation and parameter estimation. We also describe a practical example drawn from social media data - we show how to model retweet cascades using a Hawkes self-exciting process. We presents a design of the memory kernel, and results on estimating parameters and predicting popularity. The code and sample event data are available as an online appendix
point_process  tutorial  networks  dynamics  teaching 
april 2018 by rvenkat
[1703.10146] Community detection and stochastic block models: recent developments
The stochastic block model (SBM) is a random graph model with planted clusters. It is widely employed as a canonical model to study clustering and community detection, and provides generally a fertile ground to study the statistical and computational tradeoffs that arise in network and data sciences.
This note surveys the recent developments that establish the fundamental limits for community detection in the SBM, both with respect to information-theoretic and computational thresholds, and for various recovery requirements such as exact, partial and weak recovery (a.k.a., detection). The main results discussed are the phase transitions for exact recovery at the Chernoff-Hellinger threshold, the phase transition for weak recovery at the Kesten-Stigum threshold, the optimal distortion-SNR tradeoff for partial recovery, the learning of the SBM parameters and the gap between information-theoretic and computational thresholds.
The note also covers some of the algorithms developed in the quest of achieving the limits, in particular two-round algorithms via graph-splitting, semi-definite programming, linearized belief propagation, classical and nonbacktracking spectral methods. A few open problems are also discussed.
networks  block_model  teaching  community_detection  review 
april 2018 by rvenkat
[1508.01303] Modern temporal network theory: A colloquium
The power of any kind of network approach lies in the ability to simplify a complex system so that one can better understand its function as a whole. Sometimes it is beneficial, however, to include more information than in a simple graph of only nodes and links. Adding information about times of interactions can make predictions and mechanistic understanding more accurate. The drawback, however, is that there are not so many methods available, partly because temporal networks is a relatively young field, partly because it more difficult to develop such methods compared to for static networks. In this colloquium, we review the methods to analyze and model temporal networks and processes taking place on them, focusing mainly on the last three years. This includes the spreading of infectious disease, opinions, rumors, in social networks; information packets in computer networks; various types of signaling in biology, and more. We also discuss future directions.
temporal_networks  review  networks  teaching 
april 2018 by rvenkat
[1803.08823] A high-bias, low-variance introduction to Machine Learning for physicists
Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, and generalization before moving on to more advanced topics in both supervised and unsupervised learning. Topics covered in the review include ensemble models, deep learning and neural networks, clustering and data visualization, energy-based models (including MaxEnt models and Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize the many natural connections between ML and statistical physics. A notable aspect of the review is the use of Python notebooks to introduce modern ML/statistical packages to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations of supersymmetric decays of proton-proton collisions). We conclude with an extended outlook discussing possible uses of machine learning for furthering our understanding of the physical world as well as open problems in ML where physicists maybe able to contribute. (Notebooks are available at this https URL )
tutorial  review  machine_learning  deep_learning  statistical_mechanics  physics  python  teaching 
april 2018 by rvenkat
[1706.09072] Influence Networks in International Relations
Measuring influence and determining what drives it are persistent questions in political science and in network analysis more generally. Herein we focus on the domain of international relations. Our major substantive question is: How can we determine what characteristics make an actor influential? To address the topic of influence, we build on a multilinear tensor regression framework (MLTR) that captures influence relationships using a tensor generalization of a vector autoregression model. Influence relationships in that approach are captured in a pair of n x n matrices and provide measurements of how the network actions of one actor may influence the future actions of another. A limitation of the MLTR and earlier latent space approaches is that there are no direct mechanisms through which to explain why a certain actor is more or less influential than others. Our new framework, social influence regression, provides a way to statistically model the influence of one actor on another as a function of characteristics of the actors. Thus we can move beyond just estimating that an actor influences another to understanding why. To highlight the utility of this approach, we apply it to studying monthly-level conflictual events between countries as measured through the Integrated Crisis Early Warning System (ICEWS) event data project.

--Convert this to a class example or HW in a future Part II of this course?

-- Data available at Dataverse but requires some preparation. Involve others (JF,PG)?


-- for students in political science and international relations and ...
political_science  international_affairs  networks  teaching  network_data_analysis 
april 2018 by rvenkat
Empathy and well-being correlate with centrality in different social networks | PNAS
Individuals benefit from occupying central roles in social networks, but little is known about the psychological traits that predict centrality. Across four college freshman dorms (n = 193), we characterized individuals with a battery of personality questionnaires and also asked them to nominate dorm members with whom they had different types of relationships. This revealed several social networks within dorm communities with differing characteristics. In particular, additional data showed that networks varied in the degree to which nominations depend on (i) trust and (ii) shared fun and excitement. Networks more dependent upon trust were further defined by fewer connections than those more dependent on fun. Crucially, network and personality features interacted to predict individuals’ centrality: people high in well-being (i.e., life satisfaction and positive emotion) were central to networks characterized by fun, whereas people high in empathy were central to networks characterized by trust. Together, these findings provide network-based corroboration of psychological evidence that well-being is socially attractive, whereas empathy supports close relationships. More broadly, these data highlight how an individual’s personality relates to the roles that they play in sustaining their community.

--this one, Clauset et al hiring inequality, and Watts et al ATurk study for centrality discussion? (See also Newman and M.O.Jackson papers for theoretical discussions)
networks  teaching  network_data_analysis  matthew.jackson 
april 2018 by rvenkat
A Network Formation Model Based on Subgraphs by Arun G. Chandrasekhar, Matthew O. Jackson :: SSRN
We develop a new class of random-graph models for the statistical estimation of network formation that allow for substantial correlation in links. Various subgraphs (e.g., links, triangles, cliques, stars) are generated and their union results in a network. We provide estimation techniques for recovering the rates at which the underlying subgraphs were formed. We illustrate the models via a series of applications including testing for incentives to form cross-caste relationships in rural India, testing to see whether network structure is used to enforce risk-sharing, testing as to whether networks change in response to a community's exposure to microcredit, and show that these models significantly outperform stochastic block models in matching observed network characteristics. We also establish asymptotic properties of the models and various estimators, which requires proving a new Central Limit Theorem for correlated random variables.
networks  dynamics  teaching  social_networks  matthew.jackson 
april 2018 by rvenkat
[1405.0843] MuxViz: A Tool for Multilayer Analysis and Visualization of Networks
Multilayer relationships among entities and information about entities must be accompanied by the means to analyze, visualize, and obtain insights from such data. We present open-source software (muxViz) that contains a collection of algorithms for the analysis of multilayer networks, which are an important way to represent a large variety of complex systems throughout science and engineering. We demonstrate the ability of muxViz to analyze and interactively visualize multilayer data using empirical genetic, neuronal, and transportation networks. Our software is available at this https URL
networks  teaching  network_data_analysis  visualization  packages 
april 2018 by rvenkat
Lifetime-preserving reference models for characterizing spreading dynamics on temporal networks | Scientific Reports
To study how a certain network feature affects processes occurring on a temporal network, one often compares properties of the original network against those of a randomized reference model that lacks the feature in question. The randomly permuted times (PT) reference model is widely used to probe how temporal features affect spreading dynamics on temporal networks. However, PT implicitly assumes that edges and nodes are continuously active during the network sampling period – an assumption that does not always hold in real networks. We systematically analyze a recently-proposed restriction of PT that preserves node lifetimes (PTN), and a similar restriction (PTE) that also preserves edge lifetimes. We use PT, PTN, and PTE to characterize spreading dynamics on (i) synthetic networks with heterogeneous edge lifespans and tunable burstiness, and (ii) four real-world networks, including two in which nodes enter and leave the network dynamically. We find that predictions of spreading speed can change considerably with the choice of reference model. Moreover, the degree of disparity in the predictions reflects the extent of node/edge turnover, highlighting the importance of using lifetime-preserving reference models when nodes or edges are not continuously present in the network.
networks  dynamics  epidemics  temporal_networks  teaching 
april 2018 by rvenkat
Organization of feed-forward loop motifs reveals architectural principles in natural and engineered networks | Science Advances
Network motifs are significantly overrepresented subgraphs that have been proposed as building blocks for natural and engineered networks. Detailed functional analysis has been performed for many types of motif in isolation, but less is known about how motifs work together to perform complex tasks. To address this issue, we measure the aggregation of network motifs via methods that extract precisely how these structures are connected. Applying this approach to a broad spectrum of networked systems and focusing on the widespread feed-forward loop motif, we uncover striking differences in motif organization. The types of connection are often highly constrained, differ between domains, and clearly capture architectural principles. We show how this information can be used to effectively predict functionally important nodes in the metabolic network of Escherichia coli. Our findings have implications for understanding how networked systems are constructed from motif parts and elucidate constraints that guide their evolution.
i_remain_skeptical  networks  teaching  prediction  network_data_analysis 
march 2018 by rvenkat
The hot hand is back! – Department of Economics
-- Sanjurjo's compilation of blogs, articles and press featuring the Hot-hand-fallacy fallacy

-- I liked this one in particular

maybe because it was LaTeX-ed :)
dmce  teaching 
march 2018 by rvenkat
News Attention in a Mobile Era | Journal of Computer-Mediated Communication | Oxford Academic
Mobile access to the Internet is changing the way people consume information, yet we know little about the effects of this shift on news consumption. Consuming news is key to democratic citizenship, but is attention to news the same in a mobile environment? We argue that attention to news on mobile devices such as tablets and smartphones is not the same as attention to news for those on computers. Our research uses eye tracking in two lab experiments to capture the effects of mobile device use on news attention. We also conduct a large-scale study of web traffic data to provide further evidence that news attention is significantly different across computers and mobile devices.
media_studies  dmce  teaching 
march 2018 by rvenkat
SocArXiv Papers | Exposure to Opposing Views can Increase Political Polarization: Evidence from a Large-Scale Field Experiment on Social Media
There is mounting concern that social media sites contribute to political polarization by creating ``echo chambers" that insulate people from opposing views about current events. We surveyed a large sample of Democrats and Republicans who visit Twitter at least three times each week about a range of social policy issues. One week later, we randomly assigned respondents to a treatment condition in which they were offered financial incentives to follow a Twitter bot for one month that exposed them to messages produced by elected officials, organizations, and other opinion leaders with opposing political ideologies. Respondents were re-surveyed at the end of the month to measure the effect of this treatment, and at regular intervals throughout the study period to monitor treatment compliance. We find that Republicans who followed a liberal Twitter bot became substantially more conservative post-treatment, and Democrats who followed a conservative Twitter bot became slightly more liberal post-treatment. These findings have important implications for the interdisciplinary literature on political polarization as well as the emerging field of computational social science.
political_psychology  cultural_cognition  bias  public_opinion  opinion_dynamics  dmce  teaching  via:nyhan 
march 2018 by rvenkat
Infectious Disease Modeling of Social Contagion in Networks
Many behavioral phenomena have been found to spread interpersonally through social networks, in a manner similar to infectious diseases. An important difference between social contagion and traditional infectious diseases, however, is that behavioral phenomena can be acquired by non-social mechanisms as well as through social transmission. We introduce a novel theoretical framework for studying these phenomena (the SISa model) by adapting a classic disease model to include the possibility for ‘automatic’ (or ‘spontaneous’) non-social infection. We provide an example of the use of this framework by examining the spread of obesity in the Framingham Heart Study Network. The interaction assumptions of the model are validated using longitudinal network transmission data. We find that the current rate of becoming obese is 2 per year and increases by 0.5 percentage points for each obese social contact. The rate of recovering from obesity is 4 per year, and does not depend on the number of non-obese contacts. The model predicts a long-term obesity prevalence of approximately 42, and can be used to evaluate the effect of different interventions on steady-state obesity. Model predictions quantitatively reproduce the actual historical time course for the prevalence of obesity. We find that since the 1970s, the rate of recovery from obesity has remained relatively constant, while the rates of both spontaneous infection and transmission have steadily increased over time. This suggests that the obesity epidemic may be driven by increasing rates of becoming obese, both spontaneously and transmissively, rather than by decreasing rates of losing weight. A key feature of the SISa model is its ability to characterize the relative importance of social transmission by quantitatively comparing rates of spontaneous versus contagious infection. It provides a theoretical framework for studying the interpersonal spread of any state that may also arise spontaneously, such as emotions, behaviors, health states, ideas or diseases with reservoirs.

comments from cshalizi

"It has recently been suggested that certain, particular types of latent homophily, in which an unobservable trait influences both which friends one chooses and current and future behavior, may be impossible to distinguish from contagion in observational studies and hence may bias estimates of contagion and homophily [50]. The circumstances under which this is likely to be a serious source of bias (e.g., whether people, empirically, behave in these sorts of ways), and what (if anything) might be done about it (absent experimental data of the kind that some new networks studies are providing [22]) merits further study. Observational data invariably pose problems for causal inference, and require one set of assumptions or another to analyze; the plausibility of these assumptions (even of standard ones that are widely used) warrants constant review.
"The SISa model as presented here assumes that all individuals have the same probability of changing state (though not everyone will actually change state within their lifetime). It is clearly possible, however, that there is heterogeneity between individuals in these rates. We do not have sufficient data on obesity in the Framingham dataset to explore this issue, which would require observing numerous transitions between states for each individual. Exploring individual differences in acquisition rate empirically is a very interesting topic for future research, as is extending the theoretical framework we introduce to take into account individual differences."

--- For "suggested", read "proved"; the second paragraph amounts to saying "Let's just agree to ignore this".
social_networks  contagion  homophily  simulation  epidemics  networks  teaching 
march 2018 by rvenkat
--more datasets for students, in case they are interested in dynamics, especially epidemics on networks. Includes data on temporal networks.
data_sets  contagion  epidemiology  social_networks  networks  teaching 
march 2018 by rvenkat
Social Influence and Reciprocity in Online Gift Giving – Facebook Research
Giving gifts is a fundamental part of human relationships that is being affected by technology. The Internet enables people to give at the last minute and over long distances, and to observe friends giving and receiving gifts. How online gift giving spreads in social networks is therefore important to understand. We examine 1.5 million gift exchanges on Facebook and show that receiving a gift causes individuals to be 56% more likely to give a gift in the future. Additional surveys show that online gift giving was more socially acceptable to those who learned about it by observing friends’ participation instead of a non-social encouragement. Most receivers pay the gift forward instead of reciprocating directly online, although surveys revealed additional instances of direct reciprocity, where the initial gifting occurred offline. Thus, social influence promotes the spread of online gifting, which both complements and substitutes for offline gifting.

-- something about facebook experiments and the magnitude of intervention effects make me suspicious. Maybe, I'm to reading too many of Gelman's posts.
norms  influence  contagion  online_experiments  observational_studies  social_networks  networks  teaching  i_remain_skeptical 
march 2018 by rvenkat
Monophily in social networks introduces similarity among friends-of-friends | Nature Human Behaviour
The observation that individuals tend to be friends with people who are similar to themselves, commonly known as homophily, is a prominent feature of social networks. While homophily describes a bias in attribute preferences for similar others, it gives limited attention to variability. Here, we observe that attribute preferences can exhibit variation beyond what can be explained by homophily. We call this excess variation monophily to describe the presence of individuals with extreme preferences for a particular attribute possibly unrelated to their own attribute. We observe that monophily can induce a similarity among friends-of-friends without requiring any similarity among friends. To simulate homophily and monophily in synthetic networks, we propose an overdispersed extension of the classical stochastic block model. We use this model to demonstrate how homophily-based methods for predicting attributes on social networks based on friends (that is, 'the company you keep') are fundamentally different from monophily-based methods based on friends-of-friends (that is, 'the company you’re kept in'). We place particular focus on predicting gender, where homophily can be weak or non-existent in practice. These findings offer an alternative perspective on network structure and prediction, complicating the already difficult task of protecting privacy on social networks.

social_networks  privacy  network_data_analysis  latent_variable  block_model  via:clauset  networks  teaching  ? 
march 2018 by rvenkat
Network centrality: an introduction
--This, Newman et al, and Jackson et al paper for student projects?
networks  teaching  software  python  review 
march 2018 by rvenkat
[1704.03330] Food-bridging: a new network construction to unveil the principles of cooking
In this manuscript we propose, analyse, and discuss a possible new principle behind traditional cuisine: the Food-bridging hypothesis and its comparison with the food-pairing hypothesis using the same dataset and graphical models employed in the food-pairing study by Ahn et al. [Scientific Reports, 1:196 (2011)].
The Food-bridging hypothesis assumes that if two ingredients do not share a strong molecular or empirical affinity, they may become affine through a chain of pairwise affinities. That is, in a graphical model as employed by Ahn et al., a chain represents a path that joints the two ingredients, the shortest path represents the strongest pairwise chain of affinities between the two ingredients.
Food-pairing and Food-bridging are different hypotheses that may describe possible mechanisms behind the recipes of traditional cuisines. Food-pairing intensifies flavour by mixing ingredients in a recipe with similar chemical compounds, and food-bridging smoothes contrast between ingredients. Both food-pairing and food-bridging are observed in traditional cuisines, as shown in this work.
We observed four classes of cuisines according to food-pairing and food-bridging: (1) East Asian cuisines, at one extreme, tend to avoid food-pairing as well as food-bridging; and (4) Latin American cuisines, at the other extreme, follow both principles. For the two middle classes: (2) Southeastern Asian cuisines, avoid food-pairing and follow food-bridging; and (3) Western cuisines, follow food-pairing and avoid food-bridging.
culinary_history  cultural_evolution  cooking  recipe  culinary_science  networks  teaching 
march 2018 by rvenkat
[1111.6074] Flavor network and the principles of food pairing
The cultural diversity of culinary practice, as illustrated by the variety of regional cuisines, raises the question of whether there are any general patterns that determine the ingredient combinations used in food today or principles that transcend individual tastes and recipes. We introduce a flavor network that captures the flavor compounds shared by culinary ingredients. Western cuisines show a tendency to use ingredient pairs that share many flavor compounds, supporting the so-called food pairing hypothesis. By contrast, East Asian cuisines tend to avoid compound sharing ingredients. Given the increasing availability of information on food preparation, our data-driven investigation opens new avenues towards a systematic understanding of culinary practice



-- my acquired expertise in cooking is going to influence my judgment of this paper.
culinary_history  cultural_evolution  cooking  recipe  culinary_science  networks  teaching 
march 2018 by rvenkat
The British amateur who debunked the mathematics of happiness | Science | The Guardian
--Sokal's second affair?

--I've had heard my psychology students talk about use of chaos theory in psychology. Did not know that this is a small-scale industry, not a cottage industry.
bullshit  bad_science  sociology_of_science  the_guardian  teaching 
march 2018 by rvenkat
The Network Structure of Opioid Distribution on a Darknet Cryptomarket | SpringerLink

The current study is the first to examine the network structure of an encrypted online drug distribution network. It examines (1) the global network structure, (2) the local network structure, and (3) identifies those vendor characteristics that best explain variation in the network structure. In doing so, it evaluates the role of trust in online drug markets.

The study draws on a unique dataset of transaction level data from an encrypted online drug market. Structural measures and community detection analysis are used to characterize and investigate the network structure. Exponential random graph modeling is used to evaluate which vendor characteristics explain variation in purchasing patterns.

Vendors’ trustworthiness explains more variation in the overall network structure than the affordability of vendor products or the diversity of vendor product listings. This results in a highly localized network structure with a few key vendors accounting for most transactions.

The results indicate that vendors’ trustworthiness is a better predictor of vendor selection than product diversity or affordability. These results illuminate the internal market dynamics that sustain digital drug markets and highlight the importance of examining how new anonymizing technologies shape global drug distribution networks.
cryptomarket  economics  crime  social_networks  ergm  networks  teaching 
march 2018 by rvenkat
Contagion on Networks 2017
-- in case I find computer savvy human geographers and epidemiologists in my class.
contagion  epidemiology  networks  teaching 
march 2018 by rvenkat
[1803.02427] Network reconstruction and error estimation with noisy network data
Most empirical studies of networks assume that the network data we are given represent a complete and accurate picture of the nodes and edges in the system of interest, but in real-world situations this is rarely the case. More often the data only specify the network structure imperfectly -- like data in essentially every other area of empirical science, network data are prone to measurement error and noise. At the same time, the data may be richer than simple network measurements, incorporating multiple measurements, weights, lengths or strengths of edges, node or edge labels, or annotations of various kinds. Here we develop a general method for making estimates of network structure and properties from any form of network data, simple or complex, when the data are unreliable, and give example applications to a selection of social and biological networks.
mark.newman  networks  teaching 
march 2018 by rvenkat
[1703.07376] Network structure from rich but noisy data
Driven by growing interest in the sciences, industry, and among the broader public, a large number of empirical studies have been conducted in recent years of the structure of networks ranging from the internet and the world wide web to biological networks and social networks. The data produced by these experiments are often rich and multimodal, yet at the same time they may contain substantial measurement error. In practice, this means that the true network structure can differ greatly from naive estimates made from the raw data, and hence that conclusions drawn from those naive estimates may be significantly in error. In this paper we describe a technique that circumvents this problem and allows us to make optimal estimates of the true structure of networks in the presence of both richly textured data and significant measurement uncertainty. We give example applications to two different social networks, one derived from face-to-face interactions and one from self-reported friendships.
mark.newman  networks  teaching 
march 2018 by rvenkat
[1709.06005] TikZ-network manual
TikZ-network is an open source software project for visualizing graphs and networks in LaTeX. It aims to provide a simple and easy tool to create, visualize and modify complex networks. The packaged is based on the PGF/TikZ languages for producing vector graphics from a geometric/algebraic description. Particular focus is made on the software usability and interoperability with other tools. Simple networks can be directly created within LaTeX, while more complex networks can be imported from external sources (e.g. igraph, networkx, QGIS, ...). Additionally, tikz-network supports visualization of multilayer networks in two and three dimensions. The software is available at: this https URL
networks  latex  teaching 
march 2018 by rvenkat
Spectrum of Trust in Data || Data & Society
-- Interesting, people(here parents) don't trust institutions but rely of peer networks to search for knowledge and information. It is not clear whether the people have an implicit awareness of a peer reputation score through which they are able to delineate the reliable from the unreliable. I notice a similar reliance on Stack Exchange, podcasts and blogposts among my peers.
distrust_of_elites  institutions  social_epistemology  education  agnotology  media_studies  inequality  via:boyd  dmce  teaching 
march 2018 by rvenkat
The science of fake news | Science
The rise of fake news highlights the erosion of long-standing institutional bulwarks against misinformation in the internet age. Concern over the problem is global. However, much remains unknown regarding the vulnerabilities of individuals, institutions, and society to manipulations by malicious actors. A new system of safeguards is needed. Below, we discuss extant social and computer science research regarding belief in fake news and the mechanisms by which it spreads. Fake news has a long history, but we focus on unanswered scientific questions raised by the proliferation of its most recent, politically oriented incarnation. Beyond selected references in the text, suggested further reading can be found in the supplementary materials.
review  report  misinformation  disinformation  contagion  journalism  news_media  networks  dmce  teaching 
march 2018 by rvenkat
The spread of true and false news online | Science
We investigated the differential diffusion of all of the verified true and false news stories distributed on Twitter from 2006 to 2017. The data comprise ~126,000 stories tweeted by ~3 million people more than 4.5 million times. We classified news as true or false using information from six independent fact-checking organizations that exhibited 95 to 98% agreement on the classifications. Falsehood diffused significantly farther, faster, deeper, and more broadly than the truth in all categories of information, and the effects were more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends, or financial information. We found that false news was more novel than true news, which suggests that people were more likely to share novel information. Whereas false stories inspired fear, disgust, and surprise in replies, true stories inspired anticipation, sadness, joy, and trust. Contrary to conventional wisdom, robots accelerated the spread of true and false news at the same rate, implying that false news spreads more than the truth because humans, not robots, are more likely to spread it.
misinformation  contagion  social_media  social_networks  networks  teaching  via:henryfarrell 
march 2018 by rvenkat
HIV Breakthroughs and Risky Sexual Behavior* | The Quarterly Journal of Economics | Oxford Academic
Recent HIV treatment breakthroughs have lowered HIV mortality in the United States, but have also coincided with increased HIV incidence. We argue that these trends are causally linked, because new treatments have improved health and survival for the HIV +, increased their sexual activity, and thus facilitated HIV's spread. Using variation in state-level Medicaid eligibility rules as an instrument for HIV treatment, we find that treating HIV + individuals more than doubles their number of sex partners. A change of this magnitude would increase infection risk by at least 44 percent for the HIV-negative and likely have lowered their expected welfare.
health  public_policy  causal_inference  dmce  teaching  for_friends 
march 2018 by rvenkat
The Moral Hazard of Lifesaving Innovations: Naloxone Access, Opioid Abuse, and Crime by Jennifer L. Doleac, Anita Mukherjee :: SSRN
The United States is experiencing an epidemic of opioid abuse. In response, many states have increased access to Naloxone, a drug that can save lives when administered during an overdose. However, Naloxone access may unintentionally increase opioid abuse through two channels: (1) saving the lives of active drug users, who survive to continue abusing opioids, and (2) reducing the risk of death per use, thereby making riskier opioid use more appealing. By increasing the number of opioid abusers who need to fund their drug purchases, Naloxone access laws may also increase theft. We exploit the staggered timing of Naloxone access laws to estimate the total effects of these laws. We find that broadening Naloxone access led to more opioid-related emergency room visits and more opioid-related theft, with no reduction in opioid-related mortality. These effects are driven by urban areas and vary by region. We find the most detrimental effects in the Midwest, including a 14% increase in opioid-related mortality in that region. We also find suggestive evidence that broadening Naloxone access increased the use of fentanyl, a particularly potent opioid. While Naloxone has great potential as a harm-reduction strategy, our analysis is consistent with the hypothesis that broadening access to Naloxone encourages riskier behaviors with respect to opioid abuse.

--short question based on abstract alone
health  public_policy  causal_inference  dmce  teaching  for_friends 
march 2018 by rvenkat
[1802.10582] Evaluating Overfit and Underfit in Models of Network Community Structure
A common data mining task on networks is community detection, which seeks an unsupervised decomposition of a network into structural groups based on statistical regularities in the network's connectivity. Although many methods exist, the No Free Lunch theorem for community detection implies that each makes some kind of tradeoff, and no algorithm can be optimal on all inputs. Thus, different algorithms will over or underfit on different inputs, finding more, fewer, or just different communities than is optimal, and evaluation methods that use a metadata partition as a ground truth will produce misleading conclusions about general accuracy. Here, we present a broad evaluation of over and underfitting in community detection, comparing the behavior of 16 state-of-the-art community detection algorithms on a novel and structurally diverse corpus of 406 real-world networks. We find that (i) algorithms vary widely both in the number of communities they find and in their corresponding composition, given the same input, (ii) algorithms can be clustered into distinct high-level groups based on similarities of their outputs on real-world networks, and (iii) these differences induce wide variation in accuracy on link prediction and link description tasks. We introduce a new diagnostic for evaluating overfitting and underfitting in practice, and use it to roughly divide community detection methods into general and specialized learning algorithms. Across methods and inputs, Bayesian techniques based on the stochastic block model and a minimum description length approach to regularization represent the best general learning approach, but can be outperformed under specific circumstances. These results introduce both a theoretically principled approach to evaluate over and underfitting in models of network community structure and a realistic benchmark by which new methods may be evaluated and compared.
network_data_analysis  community_detection  networks  teaching  aaron.clauset 
march 2018 by rvenkat
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