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Sparse relation prediction based on hypergraph neural networks in online social networks

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Abstract

In recent years, online social networks (OSNs) have thoroughly penetrated people’s lives. Since information always flows along with various online relations in OSNs, analysing these relations becomes one of the most fundamental problems in various applications. Unfortunately, limited by the privacy concerns, data availability and the pow-law distributions of most OSNs, we can not observe enough relation links all the time, which is difficult to improve downstream tasks. To address this problem, many studies try to predict potential relations in online social networks via existing pair-wise links. However, when the observable pair-wise links are extremely sparse, most of them fail to learn the smoothness on the networks and make their proposed methods brittle. In light of this, we go beyond pair-wise relations and leverage hypergraphs to learn higher relations in the graphs. A hypergraph allows one hyperedge to connect multiple nodes, which is perfect to include more potential pair-wise links and can guarantee smooth node embeddings for better link prediction performance. In this paper, we aim at predicting potential links in sparsely observed networks. To achieve this goal, we first start from some blurry hyperedges and then proposed a novel hyperedge shrinking method to make the learned hyperedges more hierarchical. This method can learn hypergraph structure automatically from the given sparsely observed links and rely less on manual design. Following this, we further propose a novel hypergraph-based graph neural network to learn potential links in the graph. To address semantic fusion in the heterogeneous networks, we put forward multi-level reconstruction methods to preserve both specific semantics denoted by meta-paths, and high-level semantics denoted by hypergraphs. We compare our method with four state-of-the-art baselines. Extensive evaluations demonstrate that our method can achieve the best linking prediction results, especially when the networks are sparse.

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    Authors and Affiliations

    1. Northeastern University, Shenyang, China

      Yuanshen Guan & Yongjiao Sun

    2. Southeast University, Nanjing, China

      Xiangguo Sun

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    1. Yuanshen Guan
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    This article belongs to the Topical Collection: Special Issue on Decision Making in Heterogeneous Network Data Scenarios and Applications

    Guest Editors: Jianxin Li, Chengfei Liu, Ziyu Guan, and Yinghui Wu

    Xiangguo Sun is the corresponding author, and have contributed equally with the first author.

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    Guan, Y., Sun, X. & Sun, Y. Sparse relation prediction based on hypergraph neural networks in online social networks. World Wide Web 26, 7–31 (2023). https://doi.org/10.1007/s11280-021-00936-w

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