Ranking Users in Social Networks with Motif-based PageRank
Huan Zhao, Xiaogang Xu, Yangqiu Song, Dik Lun Lee, Zhao Chen, Han, Gao

TL;DR
This paper introduces motif-based PageRank (MPR), a novel method that incorporates higher-order network motifs into PageRank to improve user influence ranking in social networks, demonstrating significant performance gains.
Contribution
The paper proposes a new framework, MPR, that integrates higher-order motifs into PageRank, enhancing social network user ranking accuracy.
Findings
MPR significantly outperforms baseline methods in user ranking tasks.
Using multiple motifs improves ranking performance.
Higher-order motifs capture complex network relations effectively.
Abstract
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…
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Taxonomy
TopicsComplex Network Analysis Techniques · Opinion Dynamics and Social Influence · Advanced Graph Neural Networks
