Zulaika Zurimendi, UnaiSánchez Corcuera, RubénAlmeida, AitorLópez de Ipiña González de Artaza, Diego2024-11-072024-11-072022-05-01Zulaika, U., Sánchez-Corcuera, R., Almeida, A., & López-de-Ipiña, D. (2022). LWP-WL: Link weight prediction based on CNNs and the Weisfeiler–Lehman algorithm. Applied Soft Computing, 120. https://doi.org/10.1016/J.ASOC.2022.1086571568-494610.1016/J.ASOC.2022.108657http://hdl.handle.net/20.500.14454/1688We 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.eng© 2022 The Author(s)Graph miningLink weight predictionWeisfeiler–Lehman algorithmjournal article2024-11-07