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The Diffusion of Microfinance

TLDR
This article examined how participation in a micro-finance program diffuses through social networks and found that participants are significantly more likely to pass information on to friends and acquaintances than informed non-participants.
Abstract
We examine how participation in a microfinance program diffuses through social networks. We collected detailed demographic and social network data in 43 villages in South India before microfinance was introduced in those villages and then tracked eventual participation. We exploit exogenous variation in the importance (in a network sense) of the people who were first informed about the program, "the injection points". Microfinance participation is higher when the injection points have higher eigenvector centrality. We estimate structural models of diffusion that allow us to (i) determine the relative roles of basic information transmission versus other forms of peer influence, and (ii) distinguish information passing by participants and non-participants. We find that participants are significantly more likely to pass information on to friends and acquaintances than informed non-participants, but that information passing by non-participants is still substantial and significant, accounting for roughly a third of informedness and participation. We also find that, conditioned on being informed, an individual's decision is not significantly affected by the participation of her acquaintances.

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Who and When to Screen: Multi-Round Active Screening for Recurrent Infectious Diseases Under Uncertainty

TL;DR: This paper proposes a novel active screening model (ACTS) and algorithms to facilitate active screening for recurrent diseases (no permanent immunity) under infection uncertainty, and evaluates Full- and Fast-REMEDY on several real-world datasets which emulate human contact and find that they control diseases better than the baselines.
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Learning Embedded Representation of the Stock Correlation Matrix using Graph Machine Learning

TL;DR: This work proposes a new approach for studying nuances and relationships within the correlation network in an algorithmic way using a graph machine learning algorithm called Node2Vec, and shows that the proposed algorithm can learn an embedding from its correlation network.
Book ChapterDOI

Centrality-Preserving Exact Reductions of Multi-Layer Networks

TL;DR: This paper takes a notion of eigenvector-based centrality for a special type of MLNs (multiplex MLNs), with undirected, weighted edges, which was recently proposed in the literature, and defines and implements a framework for exact reductions for this class ofMLNs and accompanying eigen vector centrality.
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The Speed of Innovation Diffusion

TL;DR: An upper bound on the expected waiting time until a given proportion of the population has adopted that holds independently of the network structure is established.
References
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Journal ArticleDOI

Collective dynamics of small-world networks

TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.

A Contribution to the Mathematical Theory of Epidemics.

TL;DR: The present communication discussion will be limited to the case in which all members of the community are initially equally susceptible to the disease, and it will be further assumed that complete immunity is conferred by a single infection.
Journal ArticleDOI

A Simple Model of Herd Behavior

TL;DR: In this article, the authors analyze a sequential decision model in which each decision maker looks at the decisions made by previous decision makers in taking her own decision, and they show that the decision rules that are chosen by optimizing individuals will be characterized by herd behavior.
Proceedings ArticleDOI

Maximizing the spread of influence through a social network

TL;DR: An analysis framework based on submodular functions shows that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models, and suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.
Journal ArticleDOI

Epidemic Spreading in Scale-Free Networks

TL;DR: A dynamical model for the spreading of infections on scale-free networks is defined, finding the absence of an epidemic threshold and its associated critical behavior and this new epidemiological framework rationalizes data of computer viruses and could help in the understanding of other spreading phenomena on communication and social networks.