Index-Based Densest Clique Percolation Community Search in Networks - 2018


Community search is very important in graph analysis and will be used in many real applications. Within the literature, various community models are proposed. However, most of them cannot well establish the overlaps between communities that is a vital feature of real graphs. To deal with this issue, the k-clique percolation community model was proposed and has been proven effective in many applications. Motivated by this, in this Project, we have a tendency to adopt the k-clique percolation community model and study the densest clique percolation community search problem which aims to find the k-clique percolation community with the utmost k worth that contains a given set of query nodes. We tend to adopt an index-based approach to resolve this downside. Primarily based on the observation that a k-clique percolation community is a union of maximal cliques, we devise a unique compact index, DCPC-Index, to preserve the maximal cliques and their connectivity data of the input graph. With DCPC-Index, we can answerthe densest clique percolation community question efficiently. Besides, we have a tendency to also propose an index construction algorithm based mostly on the definition of DCPC-Index and further improve the algorithm in terms of efficiency and memory consumption. We tend to conduct in depth performance studies on real graphs and therefore the experimental results demonstrate the efficiency of our index-primarily based question processing algorithm and index construction algorithm.

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