g-Inspector: Recurrent Attention Model on Graph

Graph classification problem is becoming one of research hotspots in the realm of graph mining, which has been widely used in cheminformatics, bioinformatics and social network analytics. Existing approaches, such as graph kernel methods and graph Convolutional Neural Network, are facing the challen...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2022-02, Vol.34 (2), p.680-690
Main Authors: Luo, Zhiling, Cui, Yinghua, Zhao, Sha, Yin, Jianwei
Format: Article
Language:English
Subjects:
Artificial neural networks
Bioinformatics
Biological system modeling
Classification
Computational modeling
Data models
Feature extraction
Graph classification
graph mining
Kernel
recurrent neural network
reinforcement learning
Social networking (online)
Social networks
Task analysis
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Summary:Graph classification problem is becoming one of research hotspots in the realm of graph mining, which has been widely used in cheminformatics, bioinformatics and social network analytics. Existing approaches, such as graph kernel methods and graph Convolutional Neural Network, are facing the challenges of non-interpretability and high dimensionality. To address the problems, we propose a novel recurrent attention model, called g-Inspector, which applies the attention mechanism to investigate the significance of each region to make the results interpretable. It also takes a shift operation to guide the inspector agent to discover the next relevant region, so that the model sequentially loads small regions instead of the entire large graph, to solve the high dimensionality problem. The experiments conducted on standard graph datasets show the effectiveness of our g-Inspector in graph classification problems.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2020.2983689