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Social Network Analysis and Mining
Erschienen in:

01.12.2020 | Original Article

A computational study of homophily and diffusion of common knowledge on social networks based on a model of Facebook

verfasst von: Gizem Korkmaz, Chris J. Kuhlman, Joshua Goldstein, Fernando Vega-Redondo

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2020

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Abstract

In this paper, we introduce homophily to a game-theoretic model of collective action (e.g., protests) on Facebook and study the effect of homophily in individuals’ willingness to participate in collective action, i.e., their thresholds, on the emergence and spread of collective action. We use three different networks (a real Facebook network, an Erdős–Rényi random graph, and a scale-free network) and conduct computational experiments to study contagion dynamics (the size and the speed of diffusion) with respect to the level of homophily. We provide a series of parametric results on the time to achieve a specified contagion spread, on the spread of contagion at different times, and the probability of cascades. We demonstrate that these behaviors are highly nonlinear and nonmonotonic in homophily. Networks with randomly assigned thresholds result in both smaller and slower diffusion compared to the networks characterized by homophily and heterophily.
Vorheriger Artikel Dynamic wavelet fingerprint for differentiation of tweet storm types
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Fußnoten
1
Two disjoint sets of nodes such that nodes within the same set are not connected; but are connected to all nodes in the other set. Consider two sets of nodes; M and L, and let m and l denote the sizes of these sets, respectively. A complete bipartite graph, \(K_{m,l}\), is a bipartite graph that has each vertex from one set of nodes, say M, adjacent to each vertex in the other set, L. For example, \(m=1, l>1\) corresponds to a star, and \(m=2,l=2\) is a square graph.
 
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Metadaten
Titel
A computational study of homophily and diffusion of common knowledge on social networks based on a model of Facebook
verfasst von
Gizem Korkmaz
Chris J. Kuhlman
Joshua Goldstein
Fernando Vega-Redondo
Publikationsdatum
01.12.2020
Verlag
Springer Vienna
Erschienen in
Social Network Analysis and Mining / Ausgabe 1/2020
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-019-0615-5

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