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Community-guided link prediction in multiplex networks
Journal of Informetrics ( IF 3.4 ) Pub Date : 2021-06-06 , DOI: 10.1016/j.joi.2021.101178
Fatemeh Karimi , Shahriar Lotfi , Habib Izadkhah

Multiplex link prediction is the problem of finding missing links between nodes based on information from other layers. Although the link prediction problem in the online social networks is studied comprehensively, most approaches only employ internal features of the under prediction layer and do not consider additional link information from other networks. Also, many existing link prediction techniques are only based on the extracted information from links or nodes. However, the information flow in many real-world systems like social networks is considered as collaborative relations on correlated groups as an alternative for individual relations. In this research, we have proposed a Community-guided Link Prediction based on External Similarity (CLPES) method for multiplex networks in which, beside nodes and links information, community information is also employed. In our proposed method, we used an evolutionary algorithm (MOEA/D-TS) for specifying the community structure of the desired network. Next, the incorporation of internal features of each layer with a new external similarity metric (ExSim) obtains the final values for the likelihood of link formation in the network. Experiments on various real-world multiplex networks prove the capability of the proposed CLPES method for producing improved results and its superiority against other link prediction algorithms.



中文翻译:

多重网络中的社区引导链接预测

多路链路预测是根据来自其他层的信息寻找节点之间缺失的链路的问题。尽管对在线社交网络中的链接预测问题进行了全面的研究,但大多数方法只利用了下预测层的内部特征,而没有考虑来自其他网络的额外链接信息。此外,许多现有的链接预测技术仅基于从链接或节点中提取的信息。然而,许多现实世界系统(如社交网络)中的信息流被认为是相关群体上的协作关系,作为个体关系的替代方案。在这项研究中,我们提出了一种基于外部相似性(CLPES)方法的社区引导链路预测,用于多路复用网络,其中除了节点和链路信息,还使用了社区信息。在我们提出的方法中,我们使用进化算法(MOEA/D-TS)来指定所需网络的社区结构。接下来,将每一层的内部特征与新的外部相似性度量 (ExSim) 相结合,获得网络中链接形成可能性的最终值。在各种真实世界的多路复用网络上的实验证明了所提出的 CLPES 方法产生改进结果的能力及其相对于其他链路预测算法的优越性。将每一层的内部特征与新的外部相似性度量 (ExSim) 相结合,可以获得网络中链接形成可能性的最终值。在各种真实世界的多路复用网络上的实验证明了所提出的 CLPES 方法产生改进结果的能力及其相对于其他链路预测算法的优越性。将每一层的内部特征与新的外部相似性度量 (ExSim) 相结合,可以获得网络中链接形成可能性的最终值。在各种真实世界的多路复用网络上的实验证明了所提出的 CLPES 方法产生改进结果的能力及其相对于其他链路预测算法的优越性。

更新日期:2021-06-07
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