Adaptive graph convolutional collaboration networks for semi-supervised classification

• Published in: Information sciences 2022-09, Vol.611, p.262-276
• Main Authors: Fu, Sichao, Wang, Senlin, Liu, Weifeng, Liu, Baodi, Zhou, Bin, You, Xinhua, Peng, Qinmu, Jing, Xiao-Yuan
• Format: Article
• Language: English
• Subjects: Graph convolutional networks; Oversmoothing problem; Relevance (contribution) coefficient; Semi-supervised classification
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2022.08.053

Graph convolution networks (GCNs) have achieved remarkable success in processing non-Euclidean data. GCNs update the feature representations of each sample by aggregating the structure information from K-order (layer) neighborhood samples. Existing GCNs variants rely heavily on the K-th layer semantic information with K-order neighborhood information aggregating. However, semantic features from different convolution layers have distinct sample attributes. The single-layer semantic feature is only a one-sided feature representation. Besides, the semantic features of traditional GCNs will be oversmoothing with multi-layer structure information aggregates. In this paper, to solve the above-mentioned problem, we propose adaptive graph convolutional collaboration networks (AGCCNs) for the semi-supervised classification task. AGCCNs can fully use the different scales of discrimination information contained in the different convolutional layers. Specifically, AGCCNs utilize the attention mechanism to learn the relevance (contribution) coefficient of the deep semantic features from different convolution layers for the task, which aims to effectively discriminant their importance. After multiple optimizations, AGCCNs can adaptively learn the robust deep semantic features via the effective semantic fusion process between multi-layer semantic information. Compared with GCNs that only utilize the K-th layer semantic features, AGCCNs make the learned deep semantic features contain richer and more robust semantic information. What is more, our proposed AGCCNs can aggregate the appropriate K-order neighborhood information for each sample, which can relieve the oversmoothing issue of traditional GCNs and better generalize shallow GCNs to more deep layers. Abundant experimental results on several popular datasets demonstrate the superiority of our proposed AGCCNs compared with traditional GCNs.