Ranking Users in Social Networks with Motif-based PageRank

Zhao, Huan; Xu, Xiaogang; Song, Yangqiu; Lee, Dik Lun; Zhao, Chen; Gao, Han

doi:10.1109/tkde.2019.2953264

Cited by 35 publications

(29 citation statements)

References 40 publications

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“…Following [60], we let = ⊙ and = − be the adjacency matrices of the bidirectional and unidirectional social networks respectively. We use to represent the motif-induced adjacency matrix and (…”

Section: Hypergraph Constructionmentioning

confidence: 99%

Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation

Yin

et al. 2021

Proceedings of the Web Conference 2021

283

119

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Social relations are often used to improve recommendation quality when user-item interaction data is sparse in recommender systems. Most existing social recommendation models exploit pairwise relations to mine potential user preferences. However, real-life interactions among users are very complicated and user relations can be high-order. Hypergraph provides a natural way to model complex high-order relations, while its potentials for improving social recommendation are under-explored. In this paper, we fill this gap and propose a multi-channel hypergraph convolutional network to enhance social recommendation by leveraging high-order user relations. Technically, each channel in the network encodes a hypergraph that depicts a common high-order user relation pattern via hypergraph convolution. By aggregating the embeddings learned through multiple channels, we obtain comprehensive user representations to generate recommendation results. However, the aggregation operation might also obscure the inherent characteristics of different types of high-order connectivity information. To compensate for the aggregating loss, we innovatively integrate self-supervised learning into the training of the hypergraph convolutional network to regain the connectivity information with hierarchical mutual information maximization. The experimental results on multiple real-world datasets show that the proposed model outperforms the SOTA methods, and the ablation study verifies the effectiveness of the multi-channel setting and the selfsupervised task. The implementation of our model is available via https://github.com/Coder-Yu/RecQ.

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Section: Hypergraph Constructionmentioning

confidence: 99%

Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation

Yin

et al. 2021

Proceedings of the Web Conference 2021

283

119

View full text Add to dashboard Cite

show abstract

“…Benson et al proposed that important hub cities can be found in the airport networks by using specific motifs rather than edges [31]. Zhao et al proposed to merge high-level relationships into regular PageRank algorithm [32], which can significantly improve the performance of user ranking in social networks [33]. The motif is a highly active research topic and a lot more research combining realworld data could be expected in the future.…”

Section: B Motif Study Relatedmentioning

confidence: 99%

Research of Motif-Based Similarity for Link Prediction Problem

Wei

Feng

et al. 2021

IEEE Access

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The link prediction problem in the network concerns to predict the existence of links between node pairs, which is a research hotspot in different scenarios with network applications. Methods of predicting links based on network topology and structures provide a number of measurements to reveal the underlying relationship between two nodes. In this paper, a motif-based similarity index for link prediction is proposed to calculate the similarity score of two nodes concerning their local environment, which takes advantage of existing similarity definitions and the motifs. This motif-based similarity can be generalized to more complicated cases by considering different motifs and keeps the same level of computational complexity with the existing indexes. Experimental results on 9 public benchmark datasets and 1 randomly generated dataset show the effectiveness of our proposed index, and accuracies on several datasets are significantly improved. The performance of motif-based similarity suggests that considering typical motifs on networks could improve the precisions of link prediction tasks, and exploring specific structure characteristics on networks will point out an important and effective direction for more research with network methods applied.

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“…Treating scientific collaboration as a group interaction, Liang et al [34] modeled it as a hypergraph and proposed HHGBiRank. From another view of higherorder structure of networks, that is motif, Zhao et al [35] proposed motif-based PageRank.…”

Section: Related Workmentioning

confidence: 99%

M-BiRank: co-ranking developers and projects using multiple developer-project interactions in open source software community

Yan

Zhang

et al. 2020

J Wireless Com Network

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Social collaborative coding is a popular trend in software development, and such platforms as GitHub provide rich social and technical functionalities for developers to collaborate on open source projects through multiple interactions. Developers often follow popular developers and projects for learning, technical selection, and collaboration. Thus, identifying popular developers and projects is very meaningful. In this paper, we propose a multiplex bipartite network ranking model, M-BiRank, to co-rank developers and projects using multiple developer-project interactions. Firstly, multiple developer-project interactions such as commit, issue, and watch are extracted and a multiplex developer-project bipartite network is constructed. Secondly, a random layer is selected from this multiplex bipartite network and initial ranking scores are calculated for developers and projects using BiRank. Finally, initial ranking scores diffuse to other layers and mutual reinforcement is taken into consideration to iteratively calculate ranking scores of developers and projects in different layers. Experiments on real-world GitHub dataset show that M-BiRank outperforms degree centrality, traditional single layer ranking methods, and multiplex ranking method.

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Ranking Users in Social Networks with Motif-based PageRank

Cited by 35 publications

References 40 publications

Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation

Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation

Research of Motif-Based Similarity for Link Prediction Problem

M-BiRank: co-ranking developers and projects using multiple developer-project interactions in open source software community

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