A Survey on Link Prediction Models for Social Network Data

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Added by benbanbun on 2010-11-17 09:53

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Creator(s): Evan Wei Xiang

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Link prediction for social network data is a fundamental data mining task in various application domains, including social network analysis, information retrieval, recommendation systems, record linkage, marketing and bioinformatics. There are a variety of techniques for the link prediction problem, ranging from graph theory, metric learning, statistical relational learning to matrix factorization and probabilistic graphical models. In this survey, we organize the sparse related literature into a structured presentation and summarize the recent research works on the link prediction task. We categorize the current link prediction methods into three classes: the node-wise similarity based methods try to seek an appropriate distance measurement for two objects; the topological pattern based methods focus on exploiting either local or global patterns that could well describing the network; probabilistic model based methods try to learn a compact model that could abstracting the social network best. We will first review these methods, from detailed approaches to the evolution of the ideas, and then comment on their relative strengths and weaknesses. Finally, we give a brief summary on them and discuss some possible research issues.

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A Survey on Link Prediction Models for Social Network Data Link prediction for social network data is a fundamental data mining task in various application domains, including social network analysis, information retrieval, recommendation systems, record linkage, marketing and bioinformatics. There are a variety of techniques for the link prediction problem, ranging from graph theory, metric learning, statistical relational learning to matrix factorization and probabilistic graphical models. In this survey, we organize the sparse related literature into a structured presentation and summarize the recent research works on the link prediction task. We categorize the current link prediction methods into three classes: the node-wise similarity based methods try to seek an appropriate distance measurement for two objects; the topological pattern based methods focus on exploiting either local or global patterns that could well describing the network; probabilistic model based methods try to learn a compact model that could abstracting the social network best. We will first review these methods, from detailed approaches to the evolution of the ideas, and then comment on their relative strengths and weaknesses. Finally, we give a brief summary on them and discuss some possible research issues. Evan Wei Xiang