HDGCN: Dual-Channel Graph Convolutional Network With Higher-Order Information for Robust Feature Learning

Graph convolutional network (GCN) algorithms have been employed to learn graph embedding due to its inductive inference property, which is extended to GCN with higher-order information. However, the application of these higher-order information is confusing and not effectively distinguished. In addi...

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Bibliographic Details
Published in:IEEE transactions on emerging topics in computing 2024-01, Vol.12 (1), p.126-138
Main Authors: He, Meixia, Chen, Jianrui, Gong, Maoguo, Shao, Zhongshi
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Computational modeling
Data mining
Dual-channel GCN
Feature extraction
feature learning
fractional staggered sampling
higher-order GCN
Machine learning
multiple tasks prediction
Prediction algorithms
Predictive models
Representation learning
Robustness
Sampling methods
Task analysis
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Summary:Graph convolutional network (GCN) algorithms have been employed to learn graph embedding due to its inductive inference property, which is extended to GCN with higher-order information. However, the application of these higher-order information is confusing and not effectively distinguished. In addition, single channel GCN using higher-order information is weak for robust feature learning, and existing dual-channel GCNs rarely take higher-order information into account. To alleviate the above problems, we propose a dual-channel GCN with higher-order information for robust feature learning, denoted as HDGCN. First, features of positive and negative higher-order graphs are extracted that fully exploits the self-contained attributes and higher-order geometric information. Meantime, the features of original graph structure are extracted by a conventional GCN that utilizes the self-contained feature attributes. Then, node features are represented as edge features by a feature fusion function. For the selection of negative samples, a fractional staggered negative sampling method is applied, by which the trainable graph model gains better topological features. Finally, the performance on seven real-world datasets demonstrates that HDGCN obtains the state-of-the-art performance on pairwise link prediction, higher-order structure prediction, and node classification tasks. By changing the attributes of multiple tasks, it can be proved that HDGCN has good robustness.
ISSN:2168-6750
2168-6750
DOI:10.1109/TETC.2023.3238046