In this article, a novel R-convolution kernel, named the fast quantum walk kernel (FQWK), is proposed for unattributed graphs. In FQWK, the similarity of the neighborhood-pair substructure between two ...

Graph convolution machine for context-aware recommender system ... GCM simply unifies all context features as an edge, neglecting the dynamic characteristics of some contexts (e.g., time) ...

Graph Convolutional Networks (GCNs) are a class of neural networks designed to operate on graph-structured data. This repository serves as a hub for GCN-related resources, including code ...

Toward addressing this gap, we derive a precise characterization of generalization in simple graph convolution networks (GCNs) in semi-supervised * node classification on random community graphs. We ...

Keywords: load forecasting, multi-level feature fusion, neural network, time-series forecasting, graph neural networks. Citation: Feng D, Li D, Zhou Y and Wang W (2024) MLFGCN: short-term residential ...

In this article, a novel R-convolution kernel, named the fast quantum walk kernel (FQWK), is proposed for unattributed graphs. In FQWK, the similarity of the neighborhood-pair substructure between two ...