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
Canonical reference. 71% of citing Pith papers cite this work as background.
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abstract
Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions.
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representative citing papers
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.
CP-GBA distills a queryable repository of promptable subgraph triggers via graph prompt learning to achieve transferable backdoor attacks on GNNs with state-of-the-art success rates across paradigms and defenses.
GNN-Ceff is the first graph neural network model for post-layout effective capacitance prediction in VLSI circuits, delivering up to 929x speedup over serial state-of-the-art methods with improved accuracy on real 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.
The paper demonstrates a black-box model extraction attack on graph classification models that leverages binary subgraph explanations to guide Monte Carlo edge sensitivity estimation with concentration guarantees.
Neural point-forms are introduced as permutation-invariant neural layers that output learned form-comparison matrices for point clouds, with a claimed consistency proof under sampling and manifold assumptions and competitive results on synthetic and biological data.
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.
TED is a heterogeneous GNN that uses related party transaction groups and hierarchical attention to detect tax evasion, claiming significant outperformance over prior methods on two real tax datasets.
Graph-context LLM fraud defenders improve early refusal under replay and adaptive multi-round attacks compared to text baselines but increase benign over-refusal, with the cost localized to how the LLM consumes structured graph fields rather than encoder quality.
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.
Transductive Sharpening adds an entropy-minimization term on unlabeled-node predictions to the training objective for graph node classification.
Cold users dominate fake news datasets, and the User Evidence Network approximates their absent behavior data from existing user interactions to enable robust misinformation detection.
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.
Dual-stream EEG decoder separates identity and orientation to support 3D reconstruction from neural signals via circular regression and conditioned diffusion.
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.
-
Learning Dynamic Stability Landscapes in Synchronization Networks
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.
-
Cross-Paradigm Graph Backdoor Attacks with Promptable Subgraph Triggers
CP-GBA distills a queryable repository of promptable subgraph triggers via graph prompt learning to achieve transferable backdoor attacks on GNNs with state-of-the-art success rates across paradigms and defenses.
-
Effective Capacitance Modeling Using Graph Neural Networks
GNN-Ceff is the first graph neural network model for post-layout effective capacitance prediction in VLSI circuits, delivering up to 929x speedup over serial state-of-the-art methods with improved accuracy on real 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.
-
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.
-
Can Subgraph Explanations Be Weaponized to Steal Graph Neural Networks?
The paper demonstrates a black-box model extraction attack on graph classification models that leverages binary subgraph explanations to guide Monte Carlo edge sensitivity estimation with concentration guarantees.
-
Neural Point-Forms
Neural point-forms are introduced as permutation-invariant neural layers that output learned form-comparison matrices for point clouds, with a claimed consistency proof under sampling and manifold assumptions and competitive results on synthetic and biological data.
-
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.
-
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.
-
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.
-
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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TED: Related Party Transaction guided Tax Evasion Detection on Heterogeneous Graph
TED is a heterogeneous GNN that uses related party transaction groups and hierarchical attention to detect tax evasion, claiming significant outperformance over prior methods on two real tax datasets.
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Rethinking Fraud Safety Evaluation: Multi-Round Attacks Reveal Safety-Utility Tradeoffs in Graph-Context LLM Defenders
Graph-context LLM fraud defenders improve early refusal under replay and adaptive multi-round attacks compared to text baselines but increase benign over-refusal, with the cost localized to how the LLM consumes structured graph fields rather than encoder quality.
-
Fast and Featureless Node Representation Learning with Partial Pairwise Supervision
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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Graph Transductive Sharpening: Leveraging Unlabeled Predictions in Node Classification
Transductive Sharpening adds an entropy-minimization term on unlabeled-node predictions to the training objective for graph node classification.
-
Real-World Challenges in Fake News Detection: Dealing with Posts by Cold Users
Cold users dominate fake news datasets, and the User Evidence Network approximates their absent behavior data from existing user interactions to enable robust misinformation detection.
-
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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Dual-Stream EEG Decoding for 3D Visual Perception
Dual-stream EEG decoder separates identity and orientation to support 3D reconstruction from neural signals via circular regression and conditioned diffusion.
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Ligandformer: A Graph Neural Network for Predicting Compound Property with Robust Interpretation
Ligandformer is a self-attention graph neural network framework that predicts compound properties, outputs attention maps for local structural interpretation, and claims improved robustness and generalization over prior methods.
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DeepTrax: Embedding Graphs of Financial Transactions
DeepTrax learns embeddings for accounts and merchants in financial transaction graphs via methods inspired by standard graph embedding techniques, reporting strong link prediction performance and utility in fraud detection on internal datasets.
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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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Learning Robust and Task-Invariant Functional Representation from fMRI through Siamese Self-Supervised Learning
BrainSimSiam applies positive-only Siamese self-supervised learning to fMRI data to produce representations that generalize across downstream tasks and outperform supervised baselines.
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Benchmarking Swarm Optimization Algorithms for Parameter Initialization in the Quantum Approximate Optimization Algorithm
Swarm methods such as PSO, FIPSO, and QPSO yield lower approximation gaps and more stable convergence than Adam, COBYLA, or SPSA when tuning QAOA parameters on weighted MaxCut instances, especially under noise and limited shots.
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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.
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