Gauge-equivariant graph neural networks embed non-Abelian local symmetries directly into message passing for lattice gauge theories, enabling learning of nonlocal observables from local operations.
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Inductive Representation Learning on Large Graphs
16 Pith papers cite this work. Polarity classification is still indexing.
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Sheaf neural networks on the SPD manifold enable strictly more expressive second-order geometric representations than Euclidean versions and achieve SOTA results on most MoleculeNet benchmarks.
A gauge-invariant GNN using Wilson loops as inputs accurately predicts observables and simulates dynamics in Z2 and U(1) lattice gauge models.
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.
BRIDGE creates the first formal heterogeneous multi-dataset benchmark for IoT botnet detection with LODO evaluation, and TCH-Net achieves mean LODO F1 of 0.5577 while reaching F1 0.8296 on standard tests, outperforming twelve baselines.
Complex-valued GNNs using phase-equivariant activations achieve global basis invariance for distributed planar control, outperforming real-valued baselines in data efficiency, tracking, and generalization on flocking.
Independent quantum signal injection into graph DEQs yields higher test accuracy and fewer solver iterations than state-dependent or backbone-dependent injection and classical equilibrium models on NCI1, PROTEINS, and MUTAG benchmarks.
GRASP detects anomalies in system provenance graphs via self-supervised executable prediction from two-hop neighborhoods, outperforming prior PIDS on DARPA datasets by identifying all documented attacks where behaviors are learnable plus additional unlabeled suspicious activity.
MediaGraph uses co-occurrence networks from Indian news on farmer protests and a new link predictability metric to reveal source-specific reporting preferences and under-representation of farmer leaders.
K-STEMIT reduces RMSE by 21% for subsurface stratigraphy thickness estimation from radar data via a knowledge-informed spatio-temporal GNN with adaptive feature fusion and physical priors from the MAR weather model.
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.
CPGRec improves video game recommendations on Steam by balancing accuracy and diversity through category-based game connections, popularity-guided propagation, and a new negative-sample reweighting method.
CPGRec+ improves game recommendations on Steam data by reweighting player-game edges with signed preference strengths and using LLMs to generate preference-aware descriptions, yielding higher accuracy and diversity than prior models.
History-aware GNN predicts Alzheimer's progression from rs-fMRI graphs with 82.9% accuracy and 68.8% on CN-to-MCI transitions.
A hybrid clickbait detector combines OpenAI embeddings with six heuristic features, applies PCA reduction, and uses graph and tree classifiers to achieve competitive F1-scores and high ROC-AUC with reduced inference time.
A tutorial framing deep learning as a complement to optimization for sequential decision-making under uncertainty, with applications in supply chains, healthcare, and energy.
citing papers explorer
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Gauge-Equivariant Graph Neural Networks for Lattice Gauge Theories
Gauge-equivariant graph neural networks embed non-Abelian local symmetries directly into message passing for lattice gauge theories, enabling learning of nonlocal observables from local operations.
-
Sheaf Neural Networks on SPD Manifolds: Second-Order Geometric Representation Learning
Sheaf neural networks on the SPD manifold enable strictly more expressive second-order geometric representations than Euclidean versions and achieve SOTA results on most MoleculeNet benchmarks.
-
Graph Neural Networks in the Wilson Loop Representation of Abelian Lattice Gauge Theories
A gauge-invariant GNN using Wilson loops as inputs accurately predicts observables and simulates dynamics in Z2 and U(1) lattice gauge models.
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TRAVELFRAUDBENCH: A Configurable Evaluation Framework for GNN Fraud Ring Detection in Travel Networks
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.
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BRIDGE and TCH-Net: Heterogeneous Benchmark and Multi-Branch Baseline for Cross-Domain IoT Botnet Detection
BRIDGE creates the first formal heterogeneous multi-dataset benchmark for IoT botnet detection with LODO evaluation, and TCH-Net achieves mean LODO F1 of 0.5577 while reaching F1 0.8296 on standard tests, outperforming twelve baselines.
-
Complex-Valued GNNs for Distributed Basis-Invariant Control of Planar Systems
Complex-valued GNNs using phase-equivariant activations achieve global basis invariance for distributed planar control, outperforming real-valued baselines in data efficiency, tracking, and generalization on flocking.
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Quantum Injection Pathways for Implicit Graph Neural Networks
Independent quantum signal injection into graph DEQs yields higher test accuracy and fewer solver iterations than state-dependent or backbone-dependent injection and classical equilibrium models on NCI1, PROTEINS, and MUTAG benchmarks.
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GRASP -- Graph-Based Anomaly Detection Through Self-Supervised Classification
GRASP detects anomalies in system provenance graphs via self-supervised executable prediction from two-hop neighborhoods, outperforming prior PIDS on DARPA datasets by identifying all documented attacks where behaviors are learnable plus additional unlabeled suspicious activity.
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MediaGraph: A Network Theoretic Framework to Analyze Reporting Preferences in Indian News Media
MediaGraph uses co-occurrence networks from Indian news on farmer protests and a new link predictability metric to reveal source-specific reporting preferences and under-representation of farmer leaders.
-
K-STEMIT: Knowledge-Informed Spatio-Temporal Efficient Multi-Branch Graph Neural Network for Subsurface Stratigraphy Thickness Estimation from Radar Data
K-STEMIT reduces RMSE by 21% for subsurface stratigraphy thickness estimation from radar data via a knowledge-informed spatio-temporal GNN with adaptive feature fusion and physical priors from the MAR weather model.
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Communication-free Sampling and 4D Hybrid Parallelism for Scalable Mini-batch GNN Training
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.
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Category-based and Popularity-guided Video Game Recommendation: A Balance-oriented Framework
CPGRec improves video game recommendations on Steam by balancing accuracy and diversity through category-based game connections, popularity-guided propagation, and a new negative-sample reweighting method.
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CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations
CPGRec+ improves game recommendations on Steam data by reweighting player-game edges with signed preference strengths and using LLMs to generate preference-aware descriptions, yielding higher accuracy and diversity than prior models.
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Predicting Alzheimer's disease progression using rs-fMRI and a history-aware graph neural network
History-aware GNN predicts Alzheimer's progression from rs-fMRI graphs with 82.9% accuracy and 68.8% on CN-to-MCI transitions.
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Clickbait detection: quick inference with maximum impact
A hybrid clickbait detector combines OpenAI embeddings with six heuristic features, applies PCA reduction, and uses graph and tree classifiers to achieve competitive F1-scores and high ROC-AUC with reduced inference time.
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Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers
A tutorial framing deep learning as a complement to optimization for sequential decision-making under uncertainty, with applications in supply chains, healthcare, and energy.