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PageRank has been widely used to measure the authority or the influence of auser in social networks. However, conventional PageRank only makes use ofedge-based relations, which represent first-order relations between twoconnected nodes. It ignores higher-order relations that may exist betweennodes. In this paper, we propose a novel framework, motif-based PageRank (MPR),to incorporate higher-order relations into the conventional PageRankcomputation. Motifs are subgraphs consisting of a small number of nodes. We usemotifs to capture higher-order relations between nodes in a network andintroduce two methods, one linear and one non-linear, to combine PageRank withhigher-order relations. We conduct extensive experiments on three real-worldnetworks, namely, DBLP, Epinions, and Ciao. We study different types of motifs,including 3-node simple and anchor motifs, 4-node and 5-node motifs. Besidesusing single motif, we also run MPR with ensemble of multiple motifs. We alsodesign a learning task to evaluate the abilities of authority prediction withmotif-based features. All experimental results demonstrate that MPR cansignificantly improve the performance of user ranking in social networkscompared to the baseline methods.

Ranking Users in Social Networks with Motif-based PageRank