Ranking Users in Social Networks with Motif-based PageRank
| Autor: | Xiaogang Xu, Huan Zhao, Han Gao, Yangqiu Song, Dik Lun Lee, Zhao Chen |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Social and Information Networks (cs.SI)
FOS: Computer and information sciences Theoretical computer science Computer science Computer Science - Social and Information Networks 02 engineering and technology Directed graph Complex network Computer Science Applications law.invention Computational Theory and Mathematics PageRank law 020204 information systems 0202 electrical engineering electronic engineering information engineering Task analysis Motif (music) Information Systems |
| Popis: | PageRank has been widely used to measure the authority or the influence of a user in social networks. However, conventional PageRank only makes use of edge-based relations, which represent first-order relations between two connected nodes. It ignores higher-order relations that may exist between nodes. In this paper, we propose a novel framework, motif-based PageRank (MPR), to incorporate higher-order relations into the conventional PageRank computation. Motifs are subgraphs consisting of a small number of nodes. We use motifs to capture higher-order relations between nodes in a network and introduce two methods, one linear and one non-linear, to combine PageRank with higher-order relations. We conduct extensive experiments on three real-world networks, namely, DBLP, Epinions, and Ciao. We study different types of motifs, including 3-node simple and anchor motifs, 4-node and 5-node motifs. Besides using single motif, we also run MPR with ensemble of multiple motifs. We also design a learning task to evaluate the abilities of authority prediction with motif-based features. All experimental results demonstrate that MPR can significantly improve the performance of user ranking in social networks compared to the baseline methods. Accepted in TKDE 2019 |
| Databáze: | OpenAIRE |
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