A vertex similarity index using community information to improve link prediction accuracy
A vertex similarity index using community information to improve link prediction accuracy
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DOI：
10.1109/smc.2017.8122595
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发表时间：
2017-10
期刊：
影响因子：
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通讯作者：
Jingwei Wang;Yunlong Ma;Min Liu;Han Yuan;Weiming Shen;Ling Li
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文献类型：
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作者：
Jingwei Wang;Yunlong Ma;Min Liu;Han Yuan;Weiming Shen;Ling Li
链路预测在复杂网络分析中起着重要的作用。它是预测网络中未知链路或未来链路的存在。经典的链接预测方法基于公共邻居来评估顶点的相似性，并表示每个公共邻居对连接可能性的贡献相等。然而，公共邻居可能扮演不同的角色，这取决于它们是否属于同一个社区，其中顶点密集或稀疏地连接到其他社区。提出了一种结合网络拓扑信息和社团信息的相似性指标用于链接预测。所提出的方法进行了比较，与10个经典的局部相似性指数在10个现实世界的网络。实验结果表明，无论使用哪种社区检测算法，该方法都能提高链接预测的准确性。
Link prediction plays an important role in complex network analysis. It is to predict the existence of an unknown link or a future link in a network. Classical methods for link prediction evaluate the similarity of vertices based on common neighbors, and denote that every common neighbor makes equal contribution to the connection likelihood. However, common neighbors may play different roles depending on whether they belong to the same community, where vertices are densely or sparsely connected to other communities. This paper proposes a novel similarity index for link prediction which combines the topology information and community information. The proposed approach is compared with ten classical local similarity indices on ten real-world networks. The experiment results shown that the proposed approach can improve the accuracy of link prediction no matter which community detection algorithm is used.