Paper title:Paper theme:This paper studies a keyword-aware influential community query (KICQ) to find the communities with the highest influence in the network. The authors argue that existing top-r k-influential communities approach in [12] has the limitation that an appropriate k (k-core) is hard to choose for a given keyword query, and setting the influential score of a community to be the minimum weight among all members cannot accurately represent the influence of the communities. To overcome this, the authors measure the influential score of communities by combing the cohesion factor k (the cohesion factor of a connected k-core is k) and the sum of influential weight of individuals in the community. Specifically, they study two kinds of the relation types of the keywords ”And” and “OR”. To solve this problem, they first propose a baseline algorithm to find the top-r influential communities from the whole network. Then they further devise a bi-level indexing method to partition the whole graph and then find and merge the communities of the partitioned graph to obtain the final communities. The experimental result shows that bi-level indexing approach outperforms the baseline algorithm, but the improvement is only about several times which is not significant. Case studies to compare the proposed method and existing work [12] are also not provided.
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