LGIEM: Global and local node influence based community detection
LGIEM: Global and local node influence based community detection
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LGIEM：基于全局和本地节点影响力的社区检测
DOI：
10.1016/j.future.2019.12.022
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发表时间：
2020-04-01
影响因子：
7.5
通讯作者：
Al-Rodhaan，Mznah
中科院分区：
文献类型：
--
作者：
Ma，Tinghuai;Liu，Qin;Al-Rodhaan，Mznah
社区检测是复杂网络中的研究热点之一。它的目标是找到内部密集但外部稀疏连接的子图。本文提出了一种新的方法来识别最有影响力的节点，并将其作为社区的核心，从而获得初始社区。然后，通过扩展策略，将未分配的节点添加到初始社区以扩展社区。最后，对重叠社区进行合并，得到最终的社区结构。为了评估所提出的节点影响方法(LGI)的性能，使用了易感感染移除(SIR)扩散模型。通过对合成网络和真实网络的测试，LGI能够识别出具有较高影响的最佳节点，并且优于其他中心性方法。最后，实验表明，本文提出的基于影响力节点的社区发现算法(LGIEM)能够有效地发现社区，并取得了比其他现有方法更好的性能。
Community detection is one of the hot topics in the complex networks. It aims to find subgraphs that are internally dense but externally sparsely connected. In this paper, a new method is proposed to identify the most influential nodes which are considered as cores of communities and achieve the initial communities. Then, by an expansion strategy, unassigned nodes are added to initial communities to expand communities. Finally, merging overlapping communities to get the final community structure. To evaluate the performance of the proposed node influence method (LGI), the susceptible–infected–removed (SIR) diffusion model are used. Testing with the synthetic networks and real-world networks, LGI can identify best nodes with high influence and is better than other centrality methods. Finally, experiments show that our proposed community detection algorithm based on influential nodes (LGIEM) is able to detect communities efficiently, and achieves better performance compared to other recent methods.