【摘要】：在現實網絡規(guī)模不斷增大的同時,其結構也越來越復雜,針對傳統社區(qū)算法難以高效處理大規(guī)模復雜網絡數據的問題,提出了一種基于社區(qū)重疊度和模塊度增量的社區(qū)識別方法。首先根據社區(qū)節(jié)點聚集度較大的特點尋找中心節(jié)點,初步劃分社區(qū);然后將屬于多個社區(qū)的重疊節(jié)點進行拆分,進而得出社區(qū)的重疊度和模塊度增量;最后找出模塊度增量為零的節(jié)點對,從而實現對大規(guī)模復雜社區(qū)的識別。對重疊度和模塊度增量進行了分析,結果表明:所提出的算法能夠有效地識別重疊社區(qū),并具有較高的運行效率。
[Abstract]:While the scale of the real network is increasing, its structure is becoming more and more complex, aiming at the problem that the traditional community algorithm is difficult to deal with the large-scale and complex network data efficiently. This paper proposes a community identification method based on the increment of community overlap and modularity. Firstly, according to the characteristics of high concentration of community nodes, the central nodes are found and the communities are initially divided; then the overlapping nodes belonging to multiple communities are split, and then the overlap degree and modularity increment of the community are obtained. Finally, the node pairs with zero modularity increment are found to realize the identification of large-scale complex communities. The overlapping degree and modularity increment are analyzed. The results show that the proposed algorithm can effectively identify overlapping communities and has high operational efficiency.
【作者單位】： 北京信息科技大學計算機學院;
【基金】：國家自然科學基金資助項目(61671070) 網絡文化與數字傳播北京市重點實驗室開放課題(ICDD201608)
【分類號】：O157.5;TP301.6

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