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An attribute-based community search method with graph refining

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Abstract

In many complex networks, there exist diverse network topologies as well as node attributes. However, the state-of-the-art community search methods which aim to find out communities containing the query nodes only consider the network topology, but ignore the effect of node attributes. This may lead to the inaccuracy of the predicted communities. In this paper, we propose an attribute-based community search method with graph refining technique, called AGAR. First, we present the concepts of topology-based similarity and attribute-based similarity to construct a TA-graph. The TA-graph can reflect both the relations between nodes from the respect of the network topology and that of the node attributes. Then, we construct AttrTCP-index based on the structure of TA-graph. Finally, by querying the AttrTCP-index, we can find out the communities for the query nodes. Experimental results on real-world networks demonstrate AGAR is an effective and efficient community search method by considering both the network topology and node attributes.

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China (No. 61373023) and the China National Arts Fund (No. 20164129).

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Authors and Affiliations

  1. School of Software, Tsinghua University, Beijing, 100084, China

    Jingwen Shang, Chaokun Wang, Changping Wang, Gaoyang Guo & Jun Qian

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  1. Jingwen Shang
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  2. Chaokun Wang
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  3. Changping Wang
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  4. Gaoyang Guo
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  5. Jun Qian
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Correspondence to Chaokun Wang.

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Shang, J., Wang, C., Wang, C. et al. An attribute-based community search method with graph refining. J Supercomput 76, 7777–7804 (2020). https://doi.org/10.1007/s11227-017-1976-z

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