Abstract
Local community detection aims at discovering a community from a seed node by maximizing a given goodness metric. This problem has attracted a lot of attention, and various goodness metrics have been proposed in recent years. However, most existing approaches are based on the assumption that either nodes or edges in network have equal weight. In fact, the usage of weights of both nodes and edges in network can somewhat enhance the algorithmic accuracy. In this paper, we propose a novel approach for local community detection via edge weighting. In detail, we first design a new node similarity measure with full consideration of adjacent nodes’ weights. We next develop an edge weighting method based on this similarity measure. Then, we define a new goodness metric to quantify the quality of local community by integrating the edge weights. In our algorithm, we discover local community by giving priority to shell node which has maximal similarity with the current local community. We evaluate the proposed algorithm on both synthetic and real-world networks. The results of our experiment demonstrate that our algorithm is highly effective at local community detection compared to related algorithms.
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The project is supported by National Natural Science Foundation of China (61172168).
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Zhao, W., Zhang, F., Liu, J. (2016). Local Community Detection via Edge Weighting. In: Ma, S., et al. Information Retrieval Technology. AIRS 2016. Lecture Notes in Computer Science(), vol 9994. Springer, Cham. https://doi.org/10.1007/978-3-319-48051-0_6
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DOI: https://doi.org/10.1007/978-3-319-48051-0_6
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