Graph Convolutional Neural Networks via Motif-based Attention

Understand

Many real-world problems can be represented as graph-based learning problems.

• In this paper, we propose a novel framework for learning spatial and attentional convolution neural networks on arbitrary graphs.
• Different from previous convolutional neural networks on graphs, we first design a motif-matching guided subgraph normalization method to capture neighborhood information.
• Then we implement subgraph-level self-attentional layers to learn different importances from different subgraphs to solve graph classification problems.