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Discovering the densest subgraph is important in graph analysis, which has wide-ranging applications from social network community mining to the discovery of biological network modules. However, the previous algorithms neglect the connectivity of the dense subgraph since it is a challenge to give consideration to both subgraph structure and time efficiency. As a result, it may lead to isolated subgraphs...
In this paper, we propose a time-efficient and exact algorithm for the problem of discovering the densest subgraph in big data. Current algorithms for solving this problem have three problems: i) they cannot handle the dilemma between the efficiency of handing big data and the precision of the discovered densest subgraph; ii) they cannot take advantage of both the parallel computing on MapReduce and...
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