Class-Attentive Diffusion Network for Semi-Supervised Classification

• URL: http://arxiv.org/abs/2006.10222v3
• Date: Wed, 30 Dec 2020 04:35:37 GMT
• Title: Class-Attentive Diffusion Network for Semi-Supervised Classification
• Authors: Jongin Lim, Daeho Um, Hyung Jin Chang, Dae Ung Jo, Jin Young Choi
• Abstract summary: Class-Attentive Diffusion Network (CAD-Net) is a graph neural network for semi-supervised classification. In this paper, we propose a new aggregation scheme that adaptively aggregates nodes probably of the same class among K-hop neighbors. Our experiments on seven benchmark datasets consistently demonstrate the efficacy of the proposed method.
• Score: 27.433021864424266
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, graph neural networks for semi-supervised classification have been widely studied. However, existing methods only use the information of limited neighbors and do not deal with the inter-class connections in graphs. In this paper, we propose Adaptive aggregation with Class-Attentive Diffusion (AdaCAD), a new aggregation scheme that adaptively aggregates nodes probably of the same class among K-hop neighbors. To this end, we first propose a novel stochastic process, called Class-Attentive Diffusion (CAD), that strengthens attention to intra-class nodes and attenuates attention to inter-class nodes. In contrast to the existing diffusion methods with a transition matrix determined solely by the graph structure, CAD considers both the node features and the graph structure with the design of our class-attentive transition matrix that utilizes a classifier. Then, we further propose an adaptive update scheme that leverages different reflection ratios of the diffusion result for each node depending on the local class-context. As the main advantage, AdaCAD alleviates the problem of undesired mixing of inter-class features caused by discrepancies between node labels and the graph topology. Built on AdaCAD, we construct a simple model called Class-Attentive Diffusion Network (CAD-Net). Extensive experiments on seven benchmark datasets consistently demonstrate the efficacy of the proposed method and our CAD-Net significantly outperforms the state-of-the-art methods. Code is available at https://github.com/ljin0429/CAD-Net.

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