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Exploring Attention and Self-Supervised Learning Mechanism for Graph Similarity Learning
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DOI：10.1109/TNNLS.2024.3513546.png)
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Abstract
En 中文
Graph similarity estimation is a challenging task due to the complex graph structures. Though important and well-studied, three critical aspects are yet to be fully handled in a unified framework: 1) how to learn richer cross-graph interactions from a pairwise node perspective; 2) how to map the similarity matrix into a similarity score by exploiting the inherent structure in the similarity matrix; and 3) how to establish a self-supervised learning mechanism for graph similarity learning. To solve these issues, we explore multiple attention and self-supervised mechanisms for graph similarity learning in this work. More specifically, we propose a unified self-supervised nodewise attention-guided graph similarity learning framework (SNA-GSL) involving: 1) a correlation-guided contrastive learning for capturing valuable node embeddings and 2) a graph similarity learning for predicting similarity scores with multiple proposed attention mechanisms. Extensive experimental results on graph-graph regression task and graph classification task demonstrate that the proposed SNA-GSL performs favorably against state-of-the-art methods. Moreover, the remarkable achievement of our model in the graph classification task is a clear indication of its exceptional generalization capabilities. The code is available at https://github.com/IntelliDAL/Graph/SNA-GSL.
Keywords:
Similarity learning
Estimation
Contrastive learning
Correlation
Linux
Graph neural networks
Convolution
Training
Semantics
Predictive models
Graph embedding learning
graph neural network (GNN)
graph similarity
multihead self-attention
self-supervised
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7.5K
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7.2W
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