Structural diversity in social contagion

By Johan Ugander, Lars Backstrom, Cameron A. Marlow, Jon M. Kleinberg. Proceedings of the National Academy of Sciences of the United States of America, 2012

668 citations recorded by the source corpus.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/4b1386ec39e24416dce230a6fa66f011a000fc82

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Citations in the corpus

Estimating peer effects in networks with peer encouragement designs (2016, background): “Prior work on Facebook specifically (12, 13), and other related technologies (14, 15) has provided observational and quasi-experimental evidence for peer effects in initial adoption, content production, and sustained use.”
DeepCas: An End-to-end Predictor of Information Cascades (2016, background): “We include all structural features that could be generalized across data sets from recent studies of cascade prediction [10, 18, 11, 36].”
Querying k-truss community in large and dynamic graphs (2014, methodology): “Furthermore, as the communities a user participates in represent the social contexts of the user, online community search provides a useful tool for other analytical tasks, such as social circle discovery [14] and social contagion modeling [20].”
Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment (2014, background): “For example, recent work on influence in the decision to join Facebook in response to an email invitation indicates that less embedded ties are associated with greater influence, leading to a higher probability of positive response (Ugander et al. 2012).”
Understanding the Impact of Opinion Leaders’ Characteristics on Online Group Knowledge-Sharing Engagement from In-Group and Out-Group Perspectives: Evidence from a Chinese Online Knowledge-Sharing Community (2019, background): “Network average path length Lengthi,t The average shortest distance between any two members in group i on statistical day t [49,123]”
DeepInf: Social Influence Prediction with Deep Learning (2018, background): “The goal is to predict v ’s action status, given 1) the observed action statuses (black and gray circles are used to indicate “active” and “inactive”, respectively) of her near neighbors and 2) the local network she is embedded in. and the celebrated “structural diversity” in [46].”
Regular Article (2004, neutral): “non-adoption) of a service (6, 18), and have measured exposure as the number of social contacts that have adopted the service previously.”
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training (2020, background): “Examples include vertex degree, structural diversity [52], structural hole [7], k-core [2], motif [5, 32], etc.”
Self-Censorship on Facebook (2013, background): “Similarly, users with more biconnected components, the number of 2-connected subgraphs in one's social graph that we used as rough measure of distinct friend groups (Ugander et al. 2012), censored more posts (coeff.”
Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes (2014, background): “Several works attempt to model popularity dynamics us-ing traditional models for epidemic spread and diffusion of innovation [8, 23].”
Simplicial closure and higher-order link prediction (2018, background): “But much of the structure in these systems involves higher-order interactions between more than two entities at once [8, 23, 39, 44, 64]: people often communicate or interact in social groups, not just in pairs; associative relations among ideas or topics often involve the intersection of multiple c”
Dynamics of information diffusion and its applications on complex networks (2016, background): “Online social media also enable researchers and the industry to quantify factors that may be crucial to innovation diffusion besides characters of the innovation itself, such as opinion leaders, network local clustering [64,67,72,73].”
Virality Prediction and Community Structure in Social Networks (2013, background): “Existing research has attempted to characterize viral memes in terms of message content [22], temporal variation [16, 24], inﬂuential users [19, 28], ﬁnite user attention [18, 21], and local neighborhood structure [10].”
Digital Footprints: Opportunities and Challenges for Online Social Research (2014, methodology): “Ugander et al. (2012), using Facebookinternal data about users’ and nonusers’ email addresses, investigated how the probability that a user would accept an invitation changed with the number of Facebook neighbors and the number of “connected components” (connected only by links through ego).”
Identifying Influential Nodes in Large-Scale Directed Networks: The Role of Clustering (2013, background): “Empirical analysis also shows that nodes with smaller clustering have higher ability to attract new connections [32,33].”