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• W2534120657 abstract "Link prediction has been becoming an important research topic due to the rapid growth of social networks. Community-based link prediction methods are proposed to incorporate community information in order to achieve accurate prediction. However, the performance of such methods is sensitive to the selection of community detection algorithms, and they also fail to capture the correlation between link formulation and community evolution. In this paper we introduce a balanced Modularity-Maximization Link Prediction (MMLP) model to address this issue. The idea of MMLP is to integrate the formulation of two types of links into a partitioned network generative model. We proposed a probabilistic algorithm to emphasize the role of innerLinks, which correspondingly maximizes the network modularity. Then, a trade-off technique is designed to maintain the network in a stable state of equilibrium. We also present an effective feature aggregation method by exploring two variations of network features. Our proposed method can overcome the limit of several community-based methods and the extensive experimental results on both synthetic and real-world benchmark data demonstrate its effectiveness and robustness." @default.
• W2534120657 created "2016-10-28" @default.
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• W2534120657 creator A5053425174 @default.
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• W2534120657 date "2017-01-01" @default.
• W2534120657 modified "2023-10-11" @default.
• W2534120657 title "A balanced modularity maximization link prediction model in social networks" @default.
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• W2534120657 doi "https://doi.org/10.1016/j.ipm.2016.10.001" @default.
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