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| Community Tracking Algorithm Based on Similarity of Association Group Evolution |
| XU Bing1,2, ZHAO Yawei1, XU Yang, yuanxiang1,2
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1.Big Data Analysis Technology Laboratory, University of Chinese Academy of Sciences, Beijing 100049, China;
2.AI Lab Beijing Knowlegene Data Technology Company Limited, Beijing 100027, China |
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Abstract In large-scale complex networks, community structure is ubiquitous, and with the change of time, the community in the network is also changing. In order to track the changes of the community and associate the adjacent time groups to form the related groups, this paper proposes a comprehensive weighted evolutionary similarity to measure the similarity of the neighboring time groups. A method of extracting evolutionary path and generating evolutionary sequence by using "multi-part graph" is also proposed. Finally, the experimental results on a bank business data show that the algorithm is more accurate than using a single index similarity judgment.
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Received: 02 November 2018
Published: 04 July 2019
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