Tracking evolving communities in large linked networks

By John E. Hopcroft, Omar Khan, Brian Kulis, Bart Selman. Proceedings of the National Academy of Sciences of the United States of America, 2004

348 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b1ee976ceba1bdc2e2a9e51cbd893e0522cf6260

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Citations in the corpus

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].”
Community Discovery in Dynamic Networks (2017, neutral): “thus dening complex operations such as split and merge. Methods in this subcategory: (Bota et al., 2011; Bourqui et al., 2009; Brodka et al., 2013; Dhouioui and Akaichi, 2014; Greene et al., 2010; Hopcroft et al., 2004; _Ilhan and Ogud uc u, 2015; Palla et al., 2007; Rosvall and Bergstrom, 2010; Tak”
Community Detection and Mining in Social Media (2010, methodology): “Such a scheme has been applied in (Hopcroft et al., 2004; Kumar et al., 2005; Palla et al., 2007; Asur et al., 2007).”
Communities in Networks (2009, background): “snapshots rather than on a systematic approach, so it is necessary to develop community-detection methods that incorporate the network structure at multiple times (or parameter values) simultaneously [58,96,116]. More generally, this will also have important ramiﬁcations for clustering in correlated”
Quantifying social group evolution (2007, methodology): “ Our knowledge of the mechanisms governing the underlying community dynamics is limited, but is essential for a deeper understanding of the development and self-optimisation of the society as a whole [17,18,19,20,21,22]. We have developed a new algorithm based on clique percolation [23,24], that all”
Statistical properties of community structure in large social and information networks (2008, background): “Hierarchical clustering is a common approach to community identification in the social sciences [152], but it has also found application more generally [79, 89].”
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].”
Identifying emerging topics in science and technology (2014, methodology): “Hopcroft et al. (2004) identified several emerging communities using bibliographic coupling with the Citeseer database, comparing clusterings from two different time periods (1990-1998 and 1990-2001).”
Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters (2008, background): “[89, 90] are interested in the temporal evolution of communities that are robust when the input data to clustering algorithms that identify them are moderately perturbed; and Palla et al.”
Detecting emerging research fronts based on topological measures in citation networks of scientific publications (2008, background): “The use of co-citation in traditional citation analysis is also problematic (Hopcroft et al., 2004).”
Characterization of complex networks: A survey of measurements (2005, methodology): “stering [137, 138, 39, 13], which starts with N vertices and no edges. Edges are added progressively to the network in decreasing order of similarity, starting with the pair with strongest similarity [140, 127]. To evaluate the similarity associated with edge (i,j), a possibility is to use the so ca”
An event-based framework for characterizing the evolutionary behavior of interaction graphs (2007, background): “Hopcroft et al. [2004] have looked at tracking communities in interaction networks over time.”
Community detection in graphs (2009, background): “Scholars have just begun to study this problem (Asur et al., 2007; Chakrabarti et al., 2006; Chi et al., 2007; Fenn et al., 2009; Hopcroft et al., 2004; Kim and Han, 2009; Lin et al., 2008; Palla et al., 2007; Sun et al., 2007) (Section XIII).”