Enhancing Graph Neural Networks with Edge Features Through Sequential Representation

Abstract

Graph neural networks have been paid a lot attentions in recent years since many real-word data can naturally be represented by graph structures. Graph neural networks such as GCNs and GATs mainly focus on node features while ignoring edge features in graphs. However, in many graph structure data such as knowledge graphs, social networks, edge features are also important as they contain vital information about relations between nodes which are commonly ignored or simplified into binary or scalar values by existing methods. In this work we build a novel learning method on graphs called GNN-EE, i.e. GNN with Edge Enhanced, which takes both the node features and edge features into account while updating representations of graph components and can be applied to most of the common graph neural networks such as GCNs and GATs. Our GNN-EE method fits in the message-passing framework and thus is easy to generalize. In addition, we extend the random-walk-based algorithms on graphs so that they can consider both node and edge features on graphs. We use those random-walk-based algorithms as a pre-training method on graph with few initial features. We demonstrate the effectiveness and flexibility of our GNN-EE method through entity classification tasks and graph classification tasks.