An event-based framework for characterizing the evolutionary behavior of interaction graphs

By Sitaram Asur, Srinivasan Parthasarathy, Duygu Ucar. Knowledge Discovery and Data Mining, 2007

435 citations recorded by the source corpus.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/e8f403ea3b3e39e91eef8a7bb1c957ca3267d466

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In Sociology and Political Science

In Statistical and Nonlinear Physics

Citations in the corpus

Tracking the Evolution of Communities in Dynamic Social Networks (2010, methodology): “In the dynamic community ﬁnding literature there can be seen a broad consensus ( e.g. [2], [4], [5]) on the fundamental events that can be used to characterise the evolution of dynamic communities.”
Community detection in networks: A user guide (2016, background): “The simplest approach is to detect the community structure for each individual snapshot, which is a static graph [186,187,74].”
Detecting communities and their evolutions in dynamic social networks—a Bayesian approach (2011, methodology): “Asur et al. (2007) introduced a family of events on both communities and individuals to characterize evolution of communities.”
Analyzing communities and their evolutions in dynamic social networks (2009, methodology): “Asur et al. [2007] introduced a family of events on both communities and individuals to characterize evolution of communities. Sun et al. [2007] proposed a parameter-free algorithm, GraphScope, to mine time-evolving graphs where the Minimum Description Length (MDL) principle is employed to extract c”
Community Discovery in Dynamic Networks (2017, methodology): “In (Asur et al. , 2009), the authors approach the problem of identifying events in communities: MCL (a modularity based algorithm) is applied to extract communities in each timestep.”
Community detection in graphs (2009, background): “[384] explored the dynamic relationship between vertices and communities.”
Finding Statistically Significant Communities in Networks (2010, neutral): “Finally, given the recent availability of time-stamped networked datasets, it is now possible to carry out quantitative studies on the dynamics of community structure, about which very little is known [32–37].”
Facetnet: a framework for analyzing communities and their evolutions in dynamic networks (2008, background): “[1] introduced a family of events on both communities and individuals to characterize evolution of communities.”