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- W2977885717 abstract "Community search in node-attributed network has attracted extensive attention. But, most of previous researches ignore the influence among nodes. This lead to inaccuracy in many scenes where it requires communities with high expansibility. Some researches may consider the influence in community search, but they ignore the attributes cohesion of community. In this paper, we study community search problem and propose an influential attribute community search method based on both influence and attribute features. Our method can find the attributed pk-truss community with largest attribute and influence relevance score function. Most of the community models adopt k-core or k-clique. But k-core has weaker structural tightness than that of k-truss, and k-clique has a strong rule and high computation complexity. So in our method, we first present attributed pk-truss community model based on k-truss. Then we propose a novel score function to check the cohesion of the community. This function can consider both the influence features and attribute features of the community. So it can improve the accuracy of searching results. Besides, to conduct influential attribute community search in large network, our method introduces graph refining method. Experiments on two large networks with ground-truth communities show the quality evaluation, effectiveness and efficiency of our proposed method." @default.
- W2977885717 created "2019-10-10" @default.
- W2977885717 creator A5028477905 @default.
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- W2977885717 date "2019-08-01" @default.
- W2977885717 modified "2023-09-26" @default.
- W2977885717 title "Influential Attribute Community Search" @default.
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- W2977885717 doi "https://doi.org/10.1109/hpcc/smartcity/dss.2019.00369" @default.
- W2977885717 hasPublicationYear "2019" @default.
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