Elementary Subgraph Features for Link Prediction With Neural Networks

The enclosing subgraph of a target link has been proved to be effective for prediction of potential links. However, it is still unclear what topological features of the subgraph play the key role in determining the existence of links. To give a possible answer to this question, in this paper, we propose a neural network based learning method for link prediction with only 1-hop neighborhood information. In detail, we extract the one-hop neighborhood of a target link as the enclosing subgraph, then encode the subgraph into different types of topological features, and lastly feed these features to train a fully connected neural network for link prediction. The experimental results show that our proposed learning method with the 1-hop neighborhood features could outperform those heuristic-based methods and achieve nearly equal performance to the state-of-the-art learning-based method WLNM and SEAL. Furthermore, it is observed that these features can be concatenated with attribute vectors to greatly promote the link prediction performance in attributed graphs. This indicates that the topological pattern within an enclosing subgraph, which determines the existence of a possible link, can be aggregated by some elementary subgraph features.