基于种子过滤与节点影响力的并行化社区发现算法
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国网北京城区供电公司

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A Parallelized Community Discovery Algorithm Based on Seed Filtering and Node Influence
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State Grid Beijing Urban Power Supply Company

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    摘要:

    社区发现是数据挖掘领域针对复杂网络进行处理的关键问题,同时亦为后续社交网络内容推荐或与电网领域相结合,用于支撑电力领域用电用户画像的刻画,助力街区管理者更好地服务街区。现有社区发现算法常基于扩展思想,算法选取的种子节点可能相互邻接,导致扩展后的社区结构重叠性较高、划分不明显;在扩展过程中,忽略了未分配节点之间的顺序性,对可分配节点间的影响力考虑不充分。针对上述问题,本文提出基于种子过滤与节点影响力的并行化社区发现算法。该算法首先在选择关键种子时引入种子过滤机制,剔除邻接种子节点,降低社区结构重叠性;其次,在扩展过程中,以节点与社区的相似性和距离量化节点影响力,优先添加影响力高的节点;最后,对上述社区发现算法做并行化,用以处理大规模社交网络的社区划分问题。

    Abstract:

    Community discovery is a key issue in data mining to deal with complex networks. It is also recommended for subsequent social network content or combined with the power grid field to support the characterization of electricity users in the power field and help block managers to better serve block. Existing community discovery algorithms are often based on the idea of expansion. The seed nodes selected by the algorithm may be adjacent to each other, resulting in a high overlap of the expanded community structure and inconspicuous division. During the expansion process, the order between unassigned nodes is ignored. Insufficient consideration of influence among assignable nodes. Aiming at the above problems, this paper proposes a parallelized community discovery algorithm based on seed filtering and node influence. The algorithm first introduces a seed filtering mechanism when selecting key seeds to eliminate adjacent seed nodes and reduce the overlap of community structure; secondly, in the process of expansion, the similarity and distance between nodes and communities are used to quantify the influence of nodes, and priority is given to adding nodes with high influence. Finally, the above community discovery algorithm is parallelized to deal with the problem of community division in large-scale social networks.

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  • 收稿日期:2022-03-24
  • 最后修改日期:2022-05-08
  • 录用日期:2022-05-09
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