A Parallel Network Community Detection Algorithm Based on Distance Dynamics
In recent years, community detection has drawn more and more researchers' attention. With the development of Internet, the scale of network data is growing fast. It is necessary to find an effective parallel community detection algorithm for large-scale network. In this paper, we propose a nove...
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| Summary: | In recent years, community detection has drawn more and more researchers' attention. With the development of Internet, the scale of network data is growing fast. It is necessary to find an effective parallel community detection algorithm for large-scale network. In this paper, we propose a novel and parallel community detection algorithm, PCDU algorithm, based on distance dynamics. We send distances information to nodes and update distances of edges constantly, based on previous values and the unified model, which is introduced to quantify different influences from nodes and edges. It ends until the distances are stable. Then we remove some special edges from the original graph and get all subgraphs, which are the community partitions. It still inherits the advantage of uncovering small communities and outliers. Experiments based on synthetic networks and real world networks, show that our algorithm execute more efficient than stand-alone version. Since it is based on the Spark platform and designed in parallelization, the algorithm is very suitable for large datasets. We also provide a novel method taking use of double summation to calculate the NMI value of community partition result and the embedded community structure. Compared with the traditional way, it is not only as accurate as the traditional way and more efficient, but also has less space complexity. Experiments show that it is suitable for evaluating community division results in large-scale network. |
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| ISBN: | 1450349935 9781450349932 |
| ISSN: | 2473-991X |
| DOI: | 10.1145/3110025.3110135 |