Spatial Graph Convolutional Networks
This addresses a limitation in graph-based learning for domains like chemistry and image analysis, offering a novel method for incorporating spatial information.
The paper tackled the problem of Graph Convolutional Networks (GCNs) lacking neighbor ordering based on spatial positions by proposing Spatial Graph Convolutional Networks (SGCN), which outperformed state-of-the-art methods on image classification and chemical tasks.
Graph Convolutional Networks (GCNs) have recently become the primary choice for learning from graph-structured data, superseding hash fingerprints in representing chemical compounds. However, GCNs lack the ability to take into account the ordering of node neighbors, even when there is a geometric interpretation of the graph vertices that provides an order based on their spatial positions. To remedy this issue, we propose Spatial Graph Convolutional Network (SGCN) which uses spatial features to efficiently learn from graphs that can be naturally located in space. Our contribution is threefold: we propose a GCN-inspired architecture which (i) leverages node positions, (ii) is a proper generalization of both GCNs and Convolutional Neural Networks (CNNs), (iii) benefits from augmentation which further improves the performance and assures invariance with respect to the desired properties. Empirically, SGCN outperforms state-of-the-art graph-based methods on image classification and chemical tasks.