[u' @article{zulaika_zurimendi_lwp-wl_2022, title = {{LWP}-{WL}: {Link} weight prediction based on {CNNs} and the {Weisfeiler}-{Lehman} algorithm}, issn = {1568-4946}, shorttitle = {{LWP}-{WL}}, url = {https://www.sciencedirect.com/science/article/pii/S156849462200134X}, doi = {10.1016/j.asoc.2022.108657}, abstract = {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\u2019s 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.}, language = {en}, urldate = {2022-03-08}, journal = {Applied Soft Computing}, author = {Zulaika Zurimendi, Unai and S\xe1nchez-Corcuera, Rub\xe9n and Almeida, Aitor and L\xf3pez-de-Ipi\xf1a, Diego}, month = feb, year = {2022}, keywords = {FuturAAL, Graph mining, JCR6.725, Link weight prediction, Q1, SentientThings, Weisfeiler-Lehman algorithm, artificial intelligence, graph analysis, graph convolutional networks, link prediction, machine learning}, pages = {108657}, } ']