Some New Layer Architectures for Graph CNN
This work addresses the need for better graph neural network methods for researchers and practitioners in machine learning, though it appears incremental by building on existing graph CNN approaches.
The paper tackled the problem of adapting convolutional neural networks to graph-structured data by proposing new layer formulations that incorporate edge features, leading to improved classification accuracy on benchmark datasets.
While convolutional neural networks (CNNs) have recently made great strides in supervised classification of data structured on a grid (e.g. images composed of pixel grids), in several interesting datasets, the relations between features can be better represented as a general graph instead of a regular grid. Although recent algorithms that adapt CNNs to graphs have shown promising results, they mostly neglect learning explicit operations for edge features while focusing on vertex features alone. We propose new formulations for convolutional, pooling, and fully connected layers for neural networks that make more comprehensive use of the information available in multi-dimensional graphs. Using these layers led to an improvement in classification accuracy over the state-of-the-art methods on benchmark graph datasets.