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Density Sensitive Random Walk for Local Community Detection
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DOI：10.1109/ACCESS.2021.3058908.png)
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摘要
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Given a network, local community detection aims at finding the community that contains a set of query nodes (seed nodes). Random walk (RW) based algorithms have shown great success in various local community detection scenarios. Starting from the seed nodes, RW based algorithms continuously sample random walk paths to get the clustering result. However, current RW based algorithms for local clustering are faced with the following two problems. The random walker is insensitive to the community boundary and might have an unbalanced walk. These problems would have a negative effect on the clustering result of RW based algorithms. In this paper, we propose a density sensitive random walk algorithms (DSRW) for local community detection. By integrating the graph density information into the random walk process, the problems are resolved and the clustering result is improved. We provide the convergence proof of DSRW algorithm and perform extensive experiments on both real and synthetic datasets. Results show that our DSRW algorithm has achieved the state-of-the-art result in most scenarios.
Keyword:
Clustering algorithms
Detection algorithms
Convergence
Mathematical model
Licenses
Probability distribution
Linear programming
Local community detection
random walk
density sensitive
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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