DIAL, a message-passing layer that gives nodes access to graph structure through the diagonal of graph-derived operators, is introduced, which uses randomized probing to provide nodes with learnable, permutation equivariant access to diagonal entries.
A diffusion-enhanced inductive link prediction framework that combines Graph Diffusion Convolution (GDC), structural node descriptors, and neighborhood aggregation from GraphSAGE is proposed that achieves higher accuracy than the other models on the benchmark datasets.
Results show that mixed-distribution training can improve structural transfer in GNN-based centrality approximation, while identifying closeness centrality's sensitivity to topology as an open challenge.
Samra Sana, Giorgio Mantica, Saul Imbrici· 0 citations
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.
SIGIL unifies several existing regimes in graph foundation model design under a single framework that maps any attributed graph to a unified representation space of fixed dimension.
Subgraph Filtering for Fair Graph Neural Networks is proposed, a lightweight and architecture-agnostic framework that mitigates structural bias at its source and achieves consistent fairness improvements while maintaining competitive predictive performance, leading to a better fairness--accuracy trade-off than recent f...
Hao-Hui Lu, Ji-Yuan Tian, Fangyu Zhou et al.· 0 citations
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.
Aryan Mishra, Randy Martinez, Lizhen Lin· arXiv.org· 0 citations
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