GravityGraphSAGE adapts GraphSAGE with a gravity-inspired decoder to outperform prior graph deep learning methods on directed link prediction across citation networks and 16 real-world graphs.
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International Conference on Learning Representations , year=
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FSpecGNN generalizes spectral GNNs to a second-order form via node-pair lifting and bivariate spectral filters, matching Local 2-GNN expressivity while universally approximating node-pair signals and admitting scalable low-rank implementations.
Cardiac Mesh Flow generates 3D+t four-chamber cardiac meshes with anatomical correspondence and volume conditioning via one-step flow matching on multi-scale deformation fields.
NodePFN pre-trains on synthetic graphs with controllable homophily and causal feature-label models to achieve 71.27 average accuracy on 23 node classification benchmarks without graph-specific training.
GPR-GAE is a novel self-supervised graph auto-encoder using multiple Generalized PageRank filters that serves as a plug-and-play purifier achieving state-of-the-art robustness for GNNs against structural attacks.
Ex-GraphRAG replaces GNN encoders with M-GNAN for exact node-level decomposition in graph-augmented LLMs, matching black-box performance on STaRK-Prime while exposing semantic-structural mismatches that degrade multi-hop QA when low-attribution intermediaries are removed.
Provides sufficient conditions for successful distillation of combinatorial optimization tasks into DP-aligned graph neural networks under the linear representation hypothesis for the source model.
LoGraB creates fragmented graph benchmarks with controls for radius, spectral quality, noise, and coverage, while AFR reconstructs faithful graph islands from spectral patches using fidelity scoring, RANSAC-Procrustes alignment, and adaptive stitching, supported by recovery proofs and strong results
EHRAG constructs structural hyperedges from sentence co-occurrence and semantic hyperedges from entity embedding clusters, then applies hybrid diffusion plus topic-aware PPR to retrieve top-k documents, outperforming baselines on four datasets with linear indexing cost and zero token overhead.
RADAR generates query-adaptive multi-agent communication structures via conditional discrete graph diffusion guided by effective graph size, outperforming baselines on accuracy and token consumption across six benchmarks.
SCGNN uses granular-ball computing to partition nodes into groups, builds an anchor-based augmented graph, and fuses predictions with label-consistency supervision to improve semantic consistency in GNNs.
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