GraphDR-LinUCB projects contextual bandit arms onto a graph's low-frequency eigenspace to obtain the first Õ(k√T) regret bound under approximate smoothness, with a spectral predictor Γ_k that matches outcomes on five of six real datasets.
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arXiv preprint arXiv:2005.00687 , year=
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Introduces graph-to-image prediction of per-node dynamic stability landscapes in oscillator networks from topology, releases two 10k-graph datasets, and shows GNN-CNN models achieve good accuracy with cross-size generalization.
Polynomial-time max-product algorithms for exact (neuron-level) and approximate (node-level) top-K relevant walk search in GNN-LRP explanations.
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
TravelFraudBench is a new configurable benchmark for GNN-based fraud ring detection in travel networks, simulating star, clique, and chain topologies and showing GraphSAGE outperforming MLP baselines on AUC and ring recovery.
GAT uses static attention where neighbor rankings ignore the query node and thus cannot express some graph problems; GATv2 enables dynamic attention and outperforms GAT on 11 OGB and other benchmarks with equal parameters.
T3R applies multiple Rotograd matrices and a rotation technique to create surrogate gradients, enabling deeper test-time adaptation in GNNs and yielding 0.172 MAE reduction plus 9.37% relative gains on OGB benchmarks.
Message-passing algorithms compute GNN-LRP subgraph attributions in linear time w.r.t. network depth by exploiting the distributive property.
H3 is a new three-hop index that predicts physician referrals using normalized indirect pathways and outperforms heuristics and neural nets on Medicare shared-patient data in both within-period and cross-period settings.
GraphSculptor builds efficient pre-training coresets for graph self-supervised learning using combined structural and semantic diversity metrics, achieving 99.6% performance with 10% of the data.
Cerebras CS-3 achieves up to 100x speedup over CPU for SpMM and 20x for SDDMM at 90% sparsity, with performance improving for larger matrices, but becomes slower than CPU beyond 99% sparsity.
ScaleGNN uses communication-free sampling and 4D parallelism to scale mini-batch GNN training to 2048 GPUs, achieving 3.5x speedup over prior state-of-the-art on ogbn-products.
SHIRO achieves geometric mean speedups of 221.5x to 8.8x over four baselines in distributed SpMM on up to 128 GPUs by exploiting sparsity patterns and two-tier network topologies.
DGL is a graph-centric library that optimizes GNNs via generalized sparse tensor operations, transparent graph-based optimizations, and framework-neutral design, claiming superior speed and memory use over other GNN frameworks.
DeXposure-Claw combines a graph time-series foundation model for forecasting DeFi networks with rule-based monitors and data-health gates to emit regulator-aligned risk tickets, evaluated via a new six-axis benchmark on five years of real weekly data.
IO-aware GPU kernels for SpMM convolutions, degree-aware reductions, and fused attention layers deliver median speedups of 1.6-2.6x (up to 10x) and memory reductions up to 76x over DGL/PyG baselines on realistic graphs.
Contrastive FUSE learns node embeddings from partial pairwise supervision and structural signals alone by optimizing a spectral contrastive objective with a lightweight modularity approximation, yielding competitive performance and runtime gains on citation and co-purchase graphs.
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How Attentive are Graph Attention Networks?
GAT uses static attention where neighbor rankings ignore the query node and thus cannot express some graph problems; GATv2 enables dynamic attention and outperforms GAT on 11 OGB and other benchmarks with equal parameters.