Detecting highly overlapping community structure by greedy clique expansion

Lee, Conrad; Reid, Fergal; McDaid, Aaron; Hurley, Neil

doi:10.48550/arxiv.1002.1827

Cited by 19 publications

(37 citation statements)

References 29 publications

(73 reference statements)

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“…We now investigate these properties anew by modeling three social networks: Two coauthorship networks obtained from the arXiv circa 2005 [28] and from MathSciNet circa 2008 [29], as well as the email exchange network of Enron [30]. We detect their community structure with five different algorithms: A link clustering algorithm [31] (LCA), a greedy clique expansion algorithm [32] (GCE), the order statistics local optimization method [33] (OSLOM), a greedy modularity optimization of line-graphs algorithm [34] (LG), and a modified version of the classical clique percolation algorithm [28,35] (CCPA). This provides us with a total of 15 systems, from which we have selected 5 representative examples: arXiv as described by both the CCPA and LCA, Enron as described by the GCE and OSLOM algorithms, and MathSciNet as described by the GCE algorithm.…”

Section: Resultsmentioning

confidence: 99%

Growing networks of overlapping communities with internal structure

2016

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We introduce an intuitive model that describes both the emergence of community structure and the evolution of the internal structure of communities in growing social networks. The model comprises two complementary mechanisms: One mechanism accounts for the evolution of the internal link structure of a single community, and the second mechanism coordinates the growth of multiple overlapping communities. The first mechanism is based on the assumption that each node establishes links with its neighbors and introduces new nodes to the community at different rates. We demonstrate that this simple mechanism gives rise to an effective maximal degree within communities. This observation is related to the anthropological theory known as Dunbar's number, i.e., the empirical observation of a maximal number of ties which an average individual can sustain within its social groups. The second mechanism is based on a recently proposed generalization of preferential attachment to community structure, appropriately called structural preferential attachment (SPA). The combination of these two mechanisms into a single model (SPA+) allows us to reproduce a number of the global statistics of real networks: The distribution of community sizes, of node memberships, and of degrees. The SPA+ model also predicts (a) three qualitative regimes for the degree distribution within overlapping communities and (b) strong correlations between the number of communities to which a node belongs and its number of connections within each community. We present empirical evidence that support our findings in real complex networks.

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Section: Resultsmentioning

confidence: 99%

Growing networks of overlapping communities with internal structure

2016

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“…• Greedy Clique Extension method (GCE) [20]. A method for detecting highly overlapping community structure by greedy clique expansion.…”

Section: Compared Methodsmentioning

confidence: 99%

Overlapping community detection in networks based on link partitioning and partitioning around medoids

Ponomarenko¹,

Pitsoulis²,

Shamshetdinov³

2019

Preprint

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In this paper we present a new method for detecting overlapping communities in networks with a predefined number of clusters. The overlapping communities in the graph are obtained by detecting the disjoint communities in the associated line graph by means of link partitioning and partitioning around medoids. Partitioning around medoids is done through the use of a distance function defined on the set of nodes of the linear graph. In the present paper we consider the commute distance and amplified commute distance functions as distance functions. The performance of the proposed method is demonstrated by computational experiments on real life instances.

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“…One stream of global algorithms attempt to find communities by optimizing an objective function. For example, GCE [14] identifies maximal cliques as seed communities. It expands these cliques by greedily optimizing a local fitness function.…”

Section: Related Workmentioning

confidence: 99%

Overlapping Community Detection via Local Spectral Clustering

Li,

He,

Bindel

et al. 2015

Preprint

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Large graphs arise in a number of contexts and understanding their structure and extracting information from them is an important research area. Early algorithms on mining communities have focused on the global structure, and often run in time functional to the size of the entire graph. Nowadays, as we often explore networks with billions of vertices and find communities of size hundreds, it is crucial to shift our attention from macroscopic structure to microscopic structure in large networks. A growing body of work has been adopting local expansion methods in order to identify the community members from a few exemplary seed members.

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Detecting highly overlapping community structure by greedy clique expansion

Cited by 19 publications

References 29 publications

Growing networks of overlapping communities with internal structure

Growing networks of overlapping communities with internal structure

Overlapping community detection in networks based on link partitioning and partitioning around medoids

Overlapping Community Detection via Local Spectral Clustering

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