梁世娇, 柴争义. 基于节点亲密度的标签传播重叠社区发现算法[J]. 云南大学学报(自然科学版), 2020, 42(1): 58-65. doi: 10.7540/j.ynu.20190291
引用本文: 梁世娇, 柴争义. 基于节点亲密度的标签传播重叠社区发现算法[J]. 云南大学学报(自然科学版), 2020, 42(1): 58-65. doi: 10.7540/j.ynu.20190291
LIANG Shi-jiao, CHAI Zheng-yi. Label propagation overlapping community detection algorithm based on node intimacy[J]. Journal of Yunnan University: Natural Sciences Edition, 2020, 42(1): 58-65. DOI: 10.7540/j.ynu.20190291
Citation: LIANG Shi-jiao, CHAI Zheng-yi. Label propagation overlapping community detection algorithm based on node intimacy[J]. Journal of Yunnan University: Natural Sciences Edition, 2020, 42(1): 58-65. DOI: 10.7540/j.ynu.20190291

基于节点亲密度的标签传播重叠社区发现算法

Label propagation overlapping community detection algorithm based on node intimacy

  • 摘要: 针对基于标签传播的重叠社区发现算法中出现的随机性和不稳定性问题,提出了一种新的基于节点亲密度的标签传播算法. 首先,利用网络的局部信息,以模块度增量为依据,对网络中节点进行粗聚类,实现对节点的初步划分;然后,定义节点亲密度函数进行标签的更新和选择. 在人工和真实网络上对算法进行验证. 结果表明,该算法能有效地提高大规模重叠社区检测的准确性和稳定性,并且具有近乎线性的时间复杂度.

     

    Abstract: A new label propagation algorithm based on node intimacy is proposed for the randomness and instability of overlapping community discovery algorithms based on tag propagation. Firstly, based on the module degree increment, it is coarsely clustered the nodes in the network using the local information of the network. Then, it is defined the node affinity function for tag update and selection. The algorithm is validated on artificial and real networks. The results show that the algorithm can effectively improve the accuracy and stability of large-scale overlapping community detection, and has near linear time complexity.

     

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