Higher-order Graph Convolutional Networks
This work addresses a limitation in graph convolutional networks for researchers and practitioners in graph-based machine learning, though it is incremental as it builds on existing methods.
The paper tackled the problem of capturing higher-order interactions in graph-structured data for node classification by proposing Motif Convolutional Networks (MCNs), which achieved state-of-the-art results on semi-supervised node classification tasks.
Following the success of deep convolutional networks in various vision and speech related tasks, researchers have started investigating generalizations of the well-known technique for graph-structured data. A recently-proposed method called Graph Convolutional Networks has been able to achieve state-of-the-art results in the task of node classification. However, since the proposed method relies on localized first-order approximations of spectral graph convolutions, it is unable to capture higher-order interactions between nodes in the graph. In this work, we propose a motif-based graph attention model, called Motif Convolutional Networks (MCNs), which generalizes past approaches by using weighted multi-hop motif adjacency matrices to capture higher-order neighborhoods. A novel attention mechanism is used to allow each individual node to select the most relevant neighborhood to apply its filter. Experiments show that our proposed method is able to achieve state-of-the-art results on the semi-supervised node classification task.