2014
DOI: 10.1002/wics.1319
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Community detection in large‐scale networks: a survey and empirical evaluation

Abstract: Community detection is a common problem in graph data analytics that consists of finding groups of densely connected nodes with few connections to nodes outside of the group. In particular, identifying communities in large-scale networks is an important task in many scientific domains. In this review, we evaluated eight state-of-the-art and five traditional algorithms for overlapping and disjoint community detection on large-scale real-world networks with known ground-truth communities. These 13 algorithms wer… Show more

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Cited by 204 publications
(132 citation statements)
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“…Analyzing network structural data has been widely studied in the literature with most existing work focusing on community detection [21], frequent subgraph mining [10], outlier detection [2], and graph classification [11,12]. Close to our study is the line of work on graph classification.…”
Section: Related Workmentioning
confidence: 97%
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“…Analyzing network structural data has been widely studied in the literature with most existing work focusing on community detection [21], frequent subgraph mining [10], outlier detection [2], and graph classification [11,12]. Close to our study is the line of work on graph classification.…”
Section: Related Workmentioning
confidence: 97%
“…Fig.7 displays the top four subnetworks consistently discovered from all training folds. For easier visualization, we plot each subnetwork with a core node that has the highest node degree (like community hub [21]). It is found that the core nodes like T2a.R, T2a.L and TP.L indeed reside within the temporal lope-the brain region strongly impacted by Alzheimer's disease as reported in [18,27].…”
Section: Real World Datasetmentioning
confidence: 99%
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“…Figure 3 shows a graph G c for which Algorithm 1 does not converge with criterion (11). When the algorithm starts, the gain from becoming a CL is positive and the same for nodes 1, 2, and 3.…”
Section: Convergencementioning
confidence: 99%
“…A large body of research focuses on clustering methods as an analysis tool to structure, understand, and classify large data sets [8], [9]. Examples include the clustering of graphs in the scope of community detection [10], [11], [12]. Our clustering algorithm is inspired by partitioning around medoids [13].…”
Section: Introductionmentioning
confidence: 99%