Heterogeneous Hypergraph Variational Autoencoder for Link Prediction

Link prediction aims at inferring missing links or predicting future ones based on the currently observed network. This topic is important for many applications such as social media, bioinformatics and recommendation systems. Most existing methods focus on homogeneous settings and consider only low-...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence Vol. 44; no. 8; pp. 4125 - 4138
Main Authors: Fan, Haoyi, Zhang, Fengbin, Wei, Yuxuan, Li, Zuoyong, Zou, Changqing, Gao, Yue, Dai, Qionghai
Format: Journal Article
Language:English
Published: United States IEEE 01.08.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Bioinformatics
Fans
Graph theory
Graphs
Heterogeneity
Heterogeneous information network
hyperedge attention
hypergraph
link prediction
Network analysis
Network topology
Nodes
Predictions
Predictive models
Recommender systems
Semantics
Stochastic processes
Task analysis
Topology
variational inference
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:Get full text
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Summary:Link prediction aims at inferring missing links or predicting future ones based on the currently observed network. This topic is important for many applications such as social media, bioinformatics and recommendation systems. Most existing methods focus on homogeneous settings and consider only low-order pairwise relations while ignoring either the heterogeneity or high-order complex relations among different types of nodes, which tends to lead to a sub-optimal embedding result. This paper presents a method named Heterogeneous Hypergraph Variational Autoencoder (HeteHG-VAE) for link prediction in heterogeneous information networks (HINs). It first maps a conventional HIN to a heterogeneous hypergraph with a certain kind of semantics to capture both the high-order semantics and complex relations among nodes, while preserving the low-order pairwise topology information of the original HIN. Then, deep latent representations of nodes and hyperedges are learned by a Bayesian deep generative framework from the heterogeneous hypergraph in an unsupervised manner. Moreover, a hyperedge attention module is designed to learn the importance of different types of nodes in each hyperedge. The major merit of HeteHG-VAE lies in its ability of modeling multi-level relations in heterogeneous settings. Extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of the proposed method.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2021.3059313