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Found 3 papers, 1 papers with code
ADEdgeDrop: Adversarial Edge Dropping for Robust Graph Neural Networks
Employing an adversarial training framework, the edge predictor utilizes the line graph transformed from the original graph to estimate the edges to be dropped, which improves the interpretability of the edge-dropping method.
Spectral Clustering of Attributed Multi-relational Graphs
Many different algorithms have been proposed in the literature: for simple graphs, for graphs with attributes associated to nodes, and for graphs where edges represent different types of relations among nodes.
Generalized Laplacian Positional Encoding for Graph Representation Learning
Graph neural networks (GNNs) are the primary tool for processing graph-structured data.
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