  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.
