TL;DR
This work proposes LIGR (Label Informativeness-Guided Rewiring), which maximizes an information-theoretic measure quantifying how much neighbors’ labels reveal about node labels, suitable for applications requiring interpretability (biological networks, social graphs).
This work proposes Graph Diffusion Counterfactual Explanation via Inversion (GDCE-I), a discrete denoising diffusion model with a novel discrete inversion scheme that enables distribution-aware edits leveraging the whole domain edit space and qualitatively shows that GDCE-I attains interpretable in-distribution solutio...
This work introduces a novel method Schreier-Coset Graph Rewiring, a group-theoretic rewiring method that augments the input graph with a Schreier-Coset graph derived from a special linear group, creating a low-resistance bypass for long-range communication.
This work proposes Aggregation-Aware Representation Learning (AARL) to learn numerically stable and cancellation-resistant representations without sacrificing expressiveness, and formalizes this through the cancellation ratio, proving that it is fundamentally governed by graph topology.
Building on GraphLawPH, the first knowledge graph of Philippine Republic Acts, this study benchmarks three graph neural network (GNN) architectures—GAT, GCN, and GraphSAGE—and compares them to a high-dimensional Legal-BERT baseline to determine their suitability for statutory legal embedding. Using a corpus of 12,124 R...
Experiments show that replacing PageRank with alternative centralities yields similar F1-scores while offering notable runtime savings, and that GraphHD-Order remains competitive with the original GraphHD baseline while providing consistent speedups in encoding time.
HeAD-CP is proposed, a family of node-wise diffusion variants whose coefficients are determined by a label-free local-homophily estimate derived from the GNN softmax, which are most effective at extreme heterophily, intermediate heterophily, and moderate-to-high homophily, respectively, and all preserve the marginal co...
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