Core Decomposition of Massive, Information-Rich Graphs.
Encyclopedia of Social Network Analysis and Mining. 2nd Ed.(2018)
摘要
Recent advances in social and information science have shown that linked data is pervasive in our society and the world around us. Graphs have become a ubiquitous model to represent realworld structured data. They are routinely used to describe a large variety of data such as the Web, social networks, knowledge bases,(heterogeneous) information networks, biological networks, and many more. As the ability to collect data has increased geometrically in the recent years, these graphs typically count millions, or even billions, of vertices and edges. Justifiably, there has been an increasing demand for methods that are able to cope with graphs at a large scale. At the same time, the proliferation of heterogeneous data acquired from a variety of sources has given rise to more and more complex linkeddata representations. As a result, today’s realworld graphs exhibit a wide set of additional information assigned to their vertices and/or edges: weights, labels, feature vectors, probabilities of existence, probability distributions over weights or labels, time series capturing the dynamic evolution of the network, and so on (Bonchi et al. 2015, 2014a; Khan et al. 2015, 2014; Parchas et al. 2015; Ruchansky et al. 2015). These enriched graphs constitute a unique opportunity, but also a serious challenge, for improving the quality of processing algorithms. Finding dense substructures in a graph is a fundamental primitive in many graph-analysis tasks (Lee et al. 2010; Tsourakakis et al. 2013). Many different definitions of a dense subgraph have been proposed, eg, cliques, n-cliques, n-clans, k-plexes, f-groups, n-clubs, lambda sets.Most of these definitions are …
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