Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks

By Sinan Aral, Lev Muchnik, Arun Sundararajan. Proceedings of the National Academy of Sciences of the United States of America, 2009

1,341 citations recorded by the source corpus.

Node characteristics and behaviors are often correlated with the structure of social networks over time. While evidence of this type of assortative mixing and temporal clustering of behaviors among linked nodes is used to support claims of peer influence and social contagion in networks, homophily may also explain such evidence. Here we develop a dynamic matched sample estimation framework to distinguish influence and homophily effects in dynamic networks, and we apply this framework to a global instant messaging network of 27.4 million users, using data on the day-by-day adoption of a mobile service application and users' longitudinal behavioral, demographic, and geographic data. We find that previous methods overestimate peer influence in product adoption decisions in this network by 300–700%, and that homophily explains >50% of the perceived behavioral contagion. These findings and methods are essential to both our understanding of the mechanisms that drive contagions in networks and our knowledge of how to propagate or combat them in domains as diverse as epidemiology, marketing, development economics, and public health.

Subfields: Management Science and Operations Research, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/642edbf19132792fc0211d885a155af3e9405044

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Citations in the corpus

The Economic Consequences of Social Network Structure (2017, positive): “The scale of this bias can be large in practice, as pointed out by Aral, Muchnik, and Sundararajan (2009).”
Detecting, Preventing, and Mitigating Online Firestorms in Brand Communities (2019, background): “For example, Aral, Muchnik, and Sundararajan (2009) find that perceptions of similarity between customers explain more than half of the effect of behavioral contagion on new product adoption.”
Trends in Social Media: Persistence and Decay (2011, background): “Aral et al, [2] have distinguished the effects of homophily from inﬂuence as motivators for propagation.”
Social contagion theory: examining dynamic social networks and human behavior (2011, neutral): “, obesity, smoking) is available [51, 52], or when certain other techniques (such as the directional test described below) are used.”
The Dynamics of Protest Recruitment through an Online Network (2011, background): “First, we might be overestimating social influence because we do not control for demographic information and the effects of homophily in network formatio”
Influence and passivity in social media (2010, background): “Aral et al [ 9 ] have distinguished the effects of homophily from influence as motivators for propagation.”
Homophily and Contagion Are Generically Confounded in Observational Social Network Studies (2010, background): “(2004) involves, all at once, strong parametric (exponential-family) assumptions, plus the assumption that observable covariates carry all of the dependence from X to Y and A; the latter is also implicitly assumed by the matching methods of Aral et al. (2009). Whether we face the unidentifiable situ”
Quantifying Social Influence in an Online Cultural Market (2012, background): “Normative information is further strengthened by the presence of real relationships [15], versus the simple download counts that stand in for social signals in the MusicLab marketplace.”
Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics (2016, background): “Aral et al. (2009) provide intriguing evidence that a surprising proportion of the observed correlation in product adoption of social-network neighbors is due simply to their similarity rather than to social influence.”
Virality Prediction and Community Structure in Social Networks (2013, background): “Separating social contagion and homophily is difficult [41, 42], and we interpret complex contagion broadly to include homophily; we focus on how both social reinforcement and homophily effects collectively boost the trapping of memes within dense communities, not on the distinctions between them.”
Diffusion, Strategic Interaction, and Social Structure (2011, background): “Aral, Muchnik, and Sundararajan (2009) provide an idea of how large this effect can be, by carefully tracking individual characteristics and then using propensity scores (likelihoods of having neighbors with certain behaviors) to illustrate the extent to which one can over-estimate diffusion…”