梅婉婷, 张胜, 钟玲玲, 刘瑞. 基于边界节点的局部扩展社区发现算法[J]. 南昌航空大学学报(自然科学版), 2022, 36(2): 44-50, 57. DOI: 10.3969/j.issn.2096-8566.2022.02.007
引用本文: 梅婉婷, 张胜, 钟玲玲, 刘瑞. 基于边界节点的局部扩展社区发现算法[J]. 南昌航空大学学报(自然科学版), 2022, 36(2): 44-50, 57. DOI: 10.3969/j.issn.2096-8566.2022.02.007
Wan-ting MEI, Sheng ZHANG, Ling-ling ZHONG, Rui LIU. Local Extended Community Discovery Algorithm Based on Boundary Nodes[J]. Journal of nanchang hangkong university(Natural science edition), 2022, 36(2): 44-50, 57. DOI: 10.3969/j.issn.2096-8566.2022.02.007
Citation: Wan-ting MEI, Sheng ZHANG, Ling-ling ZHONG, Rui LIU. Local Extended Community Discovery Algorithm Based on Boundary Nodes[J]. Journal of nanchang hangkong university(Natural science edition), 2022, 36(2): 44-50, 57. DOI: 10.3969/j.issn.2096-8566.2022.02.007

基于边界节点的局部扩展社区发现算法

Local Extended Community Discovery Algorithm Based on Boundary Nodes

  • 摘要: 针对网络中整体信息难以获得,现有局部社区发现算法的稳定性差,预设定阈值难以获得等问题,提出一种基于边界节点的局部扩展社区发现算法(LEAB)。首先选取网络中度数最小的节点,将该节点与其邻居节点中对其吸引力最大的节点合并作为初始社区,利用社区适应度函数确定初始社区的邻接节点的社区归属,然后重复此过程,得到网络的社区划分结果。在人工生成网络和真实网络上进行实验,证明了算法可行性,与其他经典算法相比,本文提出的算法表现出较高的准确性和稳定性。

     

    Abstract: Be aimed at problems of local community discovery algorithm, such as, it is difficult to obtain the complete information of networks, the existing algorithms have low stability, and it is difficult to set thresholds, etc. We proposes a local extended community discovery algorithm based on boundary nodes (LEAB). Firstly, we select the node with lowest degree in the network, and merge it to the neighbor node which has the most attractive point among its neighbors to establish the initial community. Then, we extend current community with fitness function that determines who can join the initial community from the adjacent nodes set. Finally, repeating above steps we can get the final community discovery result of the network. Compared to existing classical algorithms in artificial networks and real networks, the proposed algorithm has higher accuracy and stability than that of others.

     

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