Hybrid Convolution Method for Graph Classification Using Hierarchical Topology Feature

Graph classification is a crucial task in the field of graph learning with numerous practical applications. Typically, the first step is to construct vertex features by the statistical information of the graph. Existing graph neural networks often adopt the one-hot degree encoding strategy to construct vertex features. Then, these features are fed into a linear layer, which outputs a low-dimensional real vector serving as the initial vertex representation for the graph model. However, the conventional approach of constructing vertex features may not be optimal. Intuitively, the method of constructing vertex features can have significant impact on the effectiveness of model. Hence, the construction of informative vertex features from the graph and the design of an efficient graph model to process these features pose great challenges. In this paper, we propose a novel method for constructing hierarchical topology vertex features and designing a hybrid convolution method to handle these features. Experimental results on public graph datasets of Social Networks, Small Molecules, and Bioinformatics demonstrate the superior performance of our method compared to baselines.