Abstract
Local community detection aims at discovering a community from a seed node without global information about the entire network structure, and various local community detection algorithms have been proposed. However, most existing algorithms either are parameter-dependent or have low accuracy. In this paper, we propose a novel approach of discovering local community using node vector model. In detail, we propose node vector model to represent nodes in graphs. Moreover, we define weighted Jaccard similarity coefficient to estimate the similarities between nodes. Based on the model and definition, local community can be detected. Our algorithm gives priority to the node which is most similar to the nodes in the current local community. We compare the proposed algorithm on both synthetic and real-world networks. The experimental results demonstrate that our algorithm is highly effective at local community detection compared to related algorithms.
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Acknowledgments
The project is supported by National Natural Science Foundation of China (61370074, 61402091), the Fundamental Research Funds for the Central Universities of China under Grant N140404012.
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Liu, J., Wang, D., Feng, S., Zhang, Y., Zhao, W. (2016). A Novel Approach of Discovering Local Community Using Node Vector Model. In: Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science(), vol 10041. Springer, Cham. https://doi.org/10.1007/978-3-319-48740-3_38
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DOI: https://doi.org/10.1007/978-3-319-48740-3_38
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