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It consists of two phases: first, we partition the network into a set of disjoint communities which are cliques, favouring larger cliques; second, we merge these communities using a hill-climbing greedy algorithm to maximize the modularity of the partition.
One popular technique is to optimize modularity, a measure of the quality of a partition in terms of the number of intracommunity and intercommunity edges.
This work proposes a new algorithm that starts by detecting disjoint cliques and then merges these to optimize modularity, and shows that this performs ...
Detecting communities in social networks based on cliques
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The second phase consists in merging these elementary groups based on clique method to obtain the final community structure. The paper is organized as follows.
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PDF | Many algorithms have been proposed for detecting disjoint communities (relatively densely connected subgraphs) in networks. One popular technique.
Apr 13, 2018 · In this paper, we propose a novel community-detection method that minimizes a new objective function, called the clique conductance function. We ...
Abstract—Discovering communities to understand and model network structures has been a fundamental problem in several fields including social networks, ...
One popular technique is to optimize modularity, a measure of the quality of a partition in terms of the number of intracommunity and intercommunity edges.
The clique percolation method is a popular approach for analyzing the overlapping community structure of networks. The term network community has no widely ...
Community detection: discovering groups in a network where individuals' ... • Find out all cliques of size k in a given network. • Construct a clique ...