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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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.
ST-TGExplainer disentangles stability and transition patterns in temporal graphs via a self-explainable TGNN guided by a disentangled information bottleneck objective to produce more faithful explanations.
Double metric learning learns two embeddings per node to build directed graphs with chain connections, yielding better performance than single metric learning for high-pT particles and accurate edge direction prediction in ATLAS ITk simulations.
GTLM injects graph-aware attention biases into LLMs using only 0.015% extra parameters, enabling native graph processing that matches 7B models with a 1B model on text-attributed graph benchmarks.
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
The survey groups attention-based GNNs into three stages—graph recurrent attention networks, graph attention networks, and graph transformers—while reviewing architectures and future directions.
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.
citing papers explorer
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GravityGraphSAGE: Link Prediction in Directed Attributed Graphs
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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Ex-GraphRAG: Interpretable Evidence Routing for Graph-Augmented LLMs
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.
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ST-TGExplainer: Disentangling Stability and Transition Patterns for Temporal GNN Interpretability
ST-TGExplainer disentangles stability and transition patterns in temporal graphs via a self-explainable TGNN guided by a disentangled information bottleneck objective to produce more faithful explanations.
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Double Metric Learning for Building Directed Graphs with Chain Connections for the ATLAS ITk Detector
Double metric learning learns two embeddings per node to build directed graphs with chain connections, yielding better performance than single metric learning for high-pT particles and accurate edge direction prediction in ATLAS ITk simulations.
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Teaching LLMs to See Graphs: Unifying Text and Structural Reasoning
GTLM injects graph-aware attention biases into LLMs using only 0.015% extra parameters, enabling native graph processing that matches 7B models with a 1B model on text-attributed graph benchmarks.
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Spectral Embeddings Leak Graph Topology: Theory, Benchmark, and Adaptive Reconstruction
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
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Attention-based graph neural networks: a survey
The survey groups attention-based GNNs into three stages—graph recurrent attention networks, graph attention networks, and graph transformers—while reviewing architectures and future directions.
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SCGNN: Semantic Consistency enhanced Graph Neural Network Guided by Granular-ball Computing
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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