LWP-WL: Link weight prediction based on CNNs and the Weisfeiler–Lehman algorithm

We present a new technique for link weight prediction, the Link Weight Prediction Weisfeiler–Lehman (LWP-WL) method that learns from graph structure features and link relationship patterns. Inspired by the Weisfeiler–Lehman Neural Machine, LWP-WL extracts an enclosing subgraph for the target link and applies a graph labelling algorithm for weighted graphs to provide an ordered subgraph adjacency matrix into a neural network. The neural network contains a Convolutional Neural Network in the first layer that applies special filters adapted to the input graph representation. An extensive evaluation is provided that demonstrates an improvement over the state-of-the-art methods in several weighted graphs. Furthermore, we conduct an ablation study to show how adding different features to our approach improves our technique’s performance. Finally, we also perform a study on the complexity and scalability of our algorithm. Unlike other approaches, LWP-WL does not rely on a specific graph heuristic and can perform well in different kinds of graphs. •A deep learning method for link weight prediction by extracting enclosing subgraphs.•Using a Weisfeiler–Lehman based node ordering algorithm over subgraphs improves neural networks efficiency.•The method applies a Convolutional Neural Network with special filters over the adjacency matrix of extracted subgraphs.•The method is compared to different baselines on several datasets.