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 solutions.
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 Republic Acts linked by 3,721 citation edges, the researchers evaluate the models as high-fidelity semantic compressors that reduce dimensionality by 91.7% while maintaining extreme representational alignment. Performance was rigorously assessed on semantic similarity detection and community discovery using retrieval metrics and structural alignment scores. The results reveal a 3.1% graph-gain for GAT in semantic retrieval (MRR=0.7870) and a massive 2,680% modularity improvement for GCN (0.4227) over the text-only baseline (0.0152), meeting established benchmarks for substantial community structure. These findings resolve the Sparsity Paradox for the corpus’s 81.79% isolated nodes, proving that transferable inductive biases from the connected minority effectively preserve semantic integrity across the entire legal graph. This study provides the first empirical benchmark for Philippine statutory GNNs, delivering guidance for selecting architectures that balance local semantic precision with global structural coherence.
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 coverage guarantee.