MSGCN: Multiplex Spatial Graph Convolution Network for Interlayer Link Weight Prediction
This addresses the challenge of predicting interlayer link weights in multiplex networks, which is an incremental advancement for domains involving graph-structured data.
The paper tackled the problem of link weight prediction in multilayer networks, which is more complex than binary link classification, by proposing the MSGCN model that spatially embeds information across layers, and it demonstrated robust and accurate performance in experiments.
Graph Neural Networks (GNNs) have been widely used for various learning tasks, ranging from node classification to link prediction. They have demonstrated excellent performance in multiple domains involving graph-structured data. However, an important category of learning tasks, namely link weight prediction, has received less emphasis due to its increased complexity compared to binary link classification. Link weight prediction becomes even more challenging when considering multilayer networks, where nodes can be interconnected across multiple layers. To address these challenges, we propose a new method named Multiplex Spatial Graph Convolution Network (MSGCN), which spatially embeds information across multiple layers to predict interlayer link weights. The MSGCN model generalizes spatial graph convolution to multiplex networks and captures the geometric structure of nodes across multiple layers. Extensive experiments using data with known interlayer link information show that the MSGCN model has robust, accurate, and generalizable link weight prediction performance across a wide variety of multiplex network structures.