Empirical Analysis of an Evolving Social Network

By Gregory Kossinets, Duncan J. Watts. Science, 2006

1,776 citations recorded by the source corpus.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/102a5c45e8371056c4acf8d40cf0177265af458e

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Citations in the corpus

Social network theory: new insights and issues for behavioral ecologists (2009, background): “…et al. 2001); (2) negative assortative partner choice (opposites attract); (3) triad closure—a tendency to become friends with friends of your friends (Kossinets and Watts 2006); and (4) a tendency for individuals to choose to span structural holes as a strategy to gain information or resources.”
Quantifying the benefits of vehicle pooling with shareability networks (2013, background): “Increasing Δ capitalizes on well-known effects of time-aggregated networks such as densification (19, 20), capturing the intuitive notion that the more patient the customers, the more opportunities for trip sharing arise (Fig.”
Preserving Privacy in Social Networks Against Neighborhood Attacks (2008, background): “Recently, as more and more social network data has been made publicly available [1], [2], [3], [4], preserving privacy in publishing social network data becomes an important concern.”
Validity Issues in the Use of Social Network Analysis with Digital Trace Data (2011, neutral): “In this sense IS research drawing on SNA is closer to the network research undertaken in physics (e.g., Ebel & Mielsch, 2002; Kossinets & Watts, 2006), than it is to network analysis in sociology (Borgatti et al., 2009).”
Fitting In or Standing Out? The Tradeoffs of Structural and Cultural Embeddedness (2015, background): “E-mail metadata also provide a window into changing network structure (Kleinbaum, Stuart, and Tushman 2013; Kossinets and Watts 2006; Srivastava 2015).”
Exploiting Temporal Dynamics in Sybil Defenses (2015, background): “Social networks, however, are constantly evolving as new relationships are formed and others fade out [13], particularly when using the more dynamic interaction-based networks that are critical for strong Sybil defense [31].”
Cooperation Prevails When Individuals Adjust Their Social Ties (2006, background): “The intuition behind this reasoning is the following: simple agents, being rational individuals with limited information, tend to interact with other agents that are close by in a social manner [18].”
Inferring friendship network structure by using mobile phone data (2009, background): “There is, however, a small but emerging thread of research examining social communication patterns based on directly observable data such as e-mail (2, 3) and call logs (4, 5).”
Simplicial closure and higher-order link prediction (2018, background): “Again, this result is consistent with both theoretical (4) and empirical (27, 31) studies of social networks, even though not all of our networks are social.”
Patterns and dynamics of users' behavior and interaction: Network analysis of an online community (2009, methodology): “…network in which relationships never decay exhibits an increasing trend that is at variance with other empirical findings (Holme et al., 2004; Kossinets & Watts, 2006) and with the behavior predicted by some recently developed network models (Barabási &Albert, 1999; Barabási et al., 2002).”
Motifs in Temporal Networks (2016, background): “There are also several analyses on the formation of triangles in a variety of social networks [9, 12, 15].”
Joint Link Prediction and Attribute Inference Using a Social-Attribute Network (2014, result): “Additionally, previous studies [Kossinets and Watts 2006; Fond and Neville 2011] have empirically demonstrated those effects on real-world social networks, providing further support for considering both network structure and node attribute information when predicting links or inferring attributes.”