DAMGNN: Deep adaptive multi-channel graph neural networks

Abstract

Recently, several studies have reported that Graph Convolutional Networks (GCN) exhibit defects in integrating node features and topological structures in graphs. Although the proposal of AMGCN compensates for the drawbacks of GCN to some extent, it still cannot solve GCN’s insufficient fusion abilities fundamentally. Thus it is essential to find a network component with stronger fusion abilities to substitute GCN. Meanwhile, a Deep Adaptive Graph Neural Network (DAGNN) proposed by Liu et al. can adaptively aggregate information from different hops of neighborhoods, which remarkably benefits its fusion abilities. To replace GCN with DAGNN network in AMGCN model and further strengthen the fusion abilities of DAGNN network itself, we make further improvements based on DAGNN model to obtain DAGNN variant. Moreover, experimentally the fusion abilities of the DAGNN variant are verified to be far stronger than GCN. And then build on that, we propose a Deep Adaptive Multi-channel Graph Neural Network (DAMGNN). The results of lots of comparative experiments on multiple benchmark datasets show that the DAMGNN model can extract relevant information from node features and topological structures to the maximum extent for fusion, thus significantly improving the accuracy of node classification.