Temporal Network Embedding for Link Prediction via VAE Joint Attention Mechanism

Network representation learning or embedding aims to project the network into a low-dimensional space that can be devoted to different network tasks. Temporal networks are an important type of network whose topological structure changes over time. Compared with methods on static networks, temporal n...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 33; no. 12; pp. 7400 - 7413
Main Authors: Jiao, Pengfei, Guo, Xuan, Jing, Xin, He, Dongxiao, Wu, Huaming, Pan, Shirui, Gong, Maoguo, Wang, Wenjun
Format: Journal Article
Language:English
Published: United States IEEE 01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Embedding
Heredity
Inheritances
Learning
Link prediction
Logic gates
Matrix decomposition
Network analysis
Network topologies
Network topology
Neural networks
Neural Networks, Computer
Parameters
Predictive models
Recurrent neural networks
Regularization
Representations
self-attention mechanism
Social networking (online)
Task analysis
temporal network embedding (TNE)
Topology
variational autoencoder (VAE)
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:Network representation learning or embedding aims to project the network into a low-dimensional space that can be devoted to different network tasks. Temporal networks are an important type of network whose topological structure changes over time. Compared with methods on static networks, temporal network embedding (TNE) methods are facing three challenges: 1) it cannot describe the temporal dependence across network snapshots; 2) the node embedding in the latent space fails to indicate changes in the network topology; and 3) it cannot avoid a lot of redundant computation via parameter inheritance on a series of snapshots. To overcome these problems, we propose a novel TNE method named temporal network embedding method based on the VAE framework (TVAE), which is based on a variational autoencoder (VAE) to capture the evolution of temporal networks for link prediction. It not only generates low-dimensional embedding vectors for nodes but also preserves the dynamic nonlinear features of temporal networks. Through the combination of a self-attention mechanism and recurrent neural networks, TVAE can update node representations and keep the temporal dependence of vectors over time. We utilize parameter inheritance to keep the new embedding close to the previous one, rather than explicitly using regularization, and thus, it is effective for large-scale networks. We evaluate our model and several baselines on synthetic data sets and real-world networks. The experimental results demonstrate that TVAE has superior performance and lower time cost compared with the baselines.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2021.3084957