A novel method for detecting new overlapping community in complex evolving networks
A novel method for detecting new overlapping community in complex evolving networks
复制标题
一种在复杂演化网络中检测新重叠社区的新方法
DOI：
10.1109/tsmc.2017.2779138
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Cong Liu
中科院分区：
文献类型：
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作者：
Jiujun Cheng;Xiao Wu;Mengchu Zhou;Shangce Gao;Zhenhua Huang;Cong Liu
在复杂网络中发现重叠社区及其演化趋势是一个重要的挑战。据我们所知，没有这样的重叠社区检测方法，表现出高归一化互信息（NMI）和F-分数，也可以预测一个重叠社区的未来考虑节点的进化，活跃性和多尺度。本文提出了一种新的方法，基于节点生命力，节点适应度的扩展建模多尺度和优先连接约束下的网络演化。首先，根据一个节点的动态，如链接的创建和销毁，我们发现节点的活力，通过比较连续的网络快照。然后，我们将其与适应度函数结合，得到一个新的目标函数联合收割机。接下来，通过优化目标函数，我们扩展最大团，重新分配重叠节点，并找到不仅匹配当前网络而且匹配未来网络版本的重叠社区。通过实验，我们表明，它的NMI和Fscore超过国家的最先进的方法在不同的条件下的重叠和连接密度。我们还验证了节点的生命力建模节点的演变的有效性。最后，我们展示了如何在现实世界中不断发展的网络中检测重叠社区。
It is an important challenge to detect an overlapping community and its evolving tendency in a complex network. To our best knowledge, there is no such an overlapping community detection method that exhibits high normalized mutual information (NMI) and F-score, and can also predict an overlapping community's future considering node evolution, activeness, and multiscaling. This paper presents a novel method based on node vitality, an extension of node fitness for modeling network evolution constrained by multiscaling and preferential attachment. First, according to a node's dynamics such as link creation and destruction, we find node vitality by comparing consecutive network snapshots. Then, we combine it with the fitness function to obtain a new objective function. Next, by optimizing the objective function, we expand maximal cliques, reassign overlapping nodes, and find the overlapping community that matches not only the current network but also the future version of the network. Through experiments, we show that its NMI and Fscore exceed those of the state-of-the-art methods under diverse conditions of overlaps and connection densities. We also validate the effectiveness of node vitality for modeling a node's evolution. Finally, we show how to detect an overlapping community in a real-world evolving network.