Graph Cross Networks with Vertex Infomax Pooling
This work addresses the challenge of multiscale feature learning in graph neural networks for researchers and practitioners in machine learning, offering incremental improvements over existing methods.
The paper tackled the problem of comprehensive feature learning from multiple scales of a graph by proposing a graph cross network (GXN) with vertex infomax pooling and feature-crossing layers, resulting in average classification accuracy improvements of 2.12% for graph classification and 1.15% for vertex classification.
We propose a novel graph cross network (GXN) to achieve comprehensive feature learning from multiple scales of a graph. Based on trainable hierarchical representations of a graph, GXN enables the interchange of intermediate features across scales to promote information flow. Two key ingredients of GXN include a novel vertex infomax pooling (VIPool), which creates multiscale graphs in a trainable manner, and a novel feature-crossing layer, enabling feature interchange across scales. The proposed VIPool selects the most informative subset of vertices based on the neural estimation of mutual information between vertex features and neighborhood features. The intuition behind is that a vertex is informative when it can maximally reflect its neighboring information. The proposed feature-crossing layer fuses intermediate features between two scales for mutual enhancement by improving information flow and enriching multiscale features at hidden layers. The cross shape of the feature-crossing layer distinguishes GXN from many other multiscale architectures. Experimental results show that the proposed GXN improves the classification accuracy by 2.12% and 1.15% on average for graph classification and vertex classification, respectively. Based on the same network, the proposed VIPool consistently outperforms other graph-pooling methods.