PromptGNN-sim uses GAT-based semantically aware neighborhood selection and structure-aware LLM prompts with bi-directional contrastive alignment to outperform prior GNN, LLM, and fusion methods on text-attributed graph datasets.
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Temporal Graph Networks for Deep Learning on Dynamic Graphs
38 Pith papers cite this work, alongside 95 external citations. Polarity classification is still indexing.
38
Pith papers citing it
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external citations · Pith
abstract
Graph Neural Networks (GNNs) have recently become increasingly popular due to their ability to learn complex systems of relations or interactions arising in a broad spectrum of problems ranging from biology and particle physics to social networks and recommendation systems. Despite the plethora of different models for deep learning on graphs, few approaches have been proposed thus far for dealing with graphs that present some sort of dynamic nature (e.g. evolving features or connectivity over time). In this paper, we present Temporal Graph Networks (TGNs), a generic, efficient framework for deep learning on dynamic graphs represented as sequences of timed events. Thanks to a novel combination of memory modules and graph-based operators, TGNs are able to significantly outperform previous approaches being at the same time more computationally efficient. We furthermore show that several previous models for learning on dynamic graphs can be cast as specific instances of our framework. We perform a detailed ablation study of different components of our framework and devise the best configuration that achieves state-of-the-art performance on several transductive and inductive prediction tasks for dynamic graphs.
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LightGBM models on citation and diversity features predict exogenous diffusion of quantum computing concepts with R² up to 0.78 while endogenous reinforcement remains largely unpredictable after growth controls, with replications in other fields.
DyGFM introduces decoupled pre-training and divergence-conditioned prompts to create the first multi-domain dynamic graph foundation model that outperforms baselines on node classification and link prediction.
DRIFT is a benchmark modeling continual graph data streams as time-varying mixtures of latent task distributions via Gaussian parameterization, revealing substantial performance degradation in existing continual learning methods under task-free continuous drift.
ATLAS achieves 12-30x faster out-of-core full-graph GNN inference on graphs up to 4B edges by switching to broadcast-based layer-wise execution with graph reordering, minimum-pending-message eviction, and GPU-accelerated tiered memory-disk hierarchy.
Event-level Shapley and feature-level Owen-value explainers for TGNNs outperform prior methods on metrics and datasets while revealing a timestamp extraction bug in TGAT.
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.
PROVFUSION fuses three complementary views of provenance data with lightweight schemes and voting to achieve higher detection accuracy and lower false positives than node- or edge-only baselines on nine benchmarks.
ChronoSpike is a spiking GNN that integrates adaptive LIF neurons with spatial attention and temporal transformers to outperform baselines on dynamic graph benchmarks by 2% F1 while training 3-10x faster with fixed parameters and stability guarantees.
Unsupervised GNN model learns local updates for approximate MaxIS on dynamic graphs, achieving competitive ratios on 200-1000 node instances and 1.00-1.18x larger solutions than other unsupervised models when generalizing to 100x larger graphs.
Graph Retention Networks extend retention to dynamic graphs to enable parallelizable training, O(1) inference, and chunkwise long-term training while delivering competitive performance with major efficiency gains.
An entity-graph MARL framework (RACHE) using R-GCN message passing and attention pooling over train-service nodes outperforms baseline algorithms in railway pricing revenue across two simulated market scenarios.
Event Relevance, built on modularized Normalized Relevance Measure, attributes ETGNN predictions to events by quantifying the entire event-induced information flow, including messages, and outperforms prior explainers on synthetic and real temporal graphs.
OPD-Evolver uses on-policy self-distillation in fast interaction and slow attribution loops to build agents with holistic memory competence, outperforming prior systems by up to 11.5% and allowing a 9B model to compete with much larger ones.
A two-stage LightGBM model on 59 features from concept networks forecasts link formation and intensity with ROC-AUC 0.95-0.967 across domains.
SA-DTS achieves up to 94% bandwidth savings and 87% lower latency in digital twin synchronization by transmitting semantic features and reconstructing states with a partitioned knowledge graph.
A 13-coordinate leakage-safe motif feature map derived from three empirical axes of temporal motif activity improves TGNN performance on link prediction and edge classification across multiple real and synthetic datasets.
COPF is a decision-layer framework for deployment-stable counterfactual fairness in online link recommendation on evolving graphs, using exposure counterfactuals, propensity logging, residual outcome indistinguishability, and graph-aware doubly robust estimators, supported by a noisy transfer theore
DSRD unifies temporal and structural adaptation for dynamic graphs via a single recurrent retentive state with learnable time-sensitivity parameters in the decay kernels.
Introduces HCIM framework with recency weighting, LSTM trajectories, and temporal attention to enhance static signed GNNs, showing statistically significant gains on Bitcoin, Reddit, and synthetic TSN datasets.
Diagnoses attention dispersion in CTDG Transformers under temporal shift and introduces differential attention to suppress common signals and achieve SOTA on shifted benchmarks.
Using two years of Lightning Network gossip data, a simple MLP predicts channel closure type (open/mutual/forced) with 0.38 macro F1, outperforming graph-based models.
FAME models scientific topic trajectories in continuous time to forecast paper impact more accurately than LLMs by aligning manuscripts with field momentum in a dynamic latent space.
PRISM iteratively transforms semantic priors into behavior-conditioned posteriors via cross-modal refinement to improve representation learning on dynamic text-attributed graphs.
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Forecasting Conceptual Diffusion in Science: The Case of Quantum Computing
LightGBM models on citation and diversity features predict exogenous diffusion of quantum computing concepts with R² up to 0.78 while endogenous reinforcement remains largely unpredictable after growth controls, with replications in other fields.
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Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models
DyGFM introduces decoupled pre-training and divergence-conditioned prompts to create the first multi-domain dynamic graph foundation model that outperforms baselines on node classification and link prediction.
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DRIFT: A Benchmark for Task-Free Continual Graph Learning with Continuous Distribution Shifts
DRIFT is a benchmark modeling continual graph data streams as time-varying mixtures of latent task distributions via Gaussian parameterization, revealing substantial performance degradation in existing continual learning methods under task-free continuous drift.
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ATLAS: Efficient Out-of-Core Inference for Billion-Scale Graph Neural Networks
ATLAS achieves 12-30x faster out-of-core full-graph GNN inference on graphs up to 4B edges by switching to broadcast-based layer-wise execution with graph reordering, minimum-pending-message eviction, and GPU-accelerated tiered memory-disk hierarchy.
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Explaining Temporal Graph Predictions With Shapley Values
Event-level Shapley and feature-level Owen-value explainers for TGNNs outperform prior methods on metrics and datasets while revealing a timestamp extraction bug in TGAT.
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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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Beyond Nodes vs. Edges: A Multi-View Fusion Framework for Provenance-Based Intrusion Detection
PROVFUSION fuses three complementary views of provenance data with lightweight schemes and voting to achieve higher detection accuracy and lower false positives than node- or edge-only baselines on nine benchmarks.
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ChronoSpike: An Adaptive Spiking Graph Neural Network for Dynamic Graphs
ChronoSpike is a spiking GNN that integrates adaptive LIF neurons with spatial attention and temporal transformers to outperform baselines on dynamic graph benchmarks by 2% F1 while training 3-10x faster with fixed parameters and stability guarantees.
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Unsupervised Learning of Local Updates for Maximum Independent Set in Dynamic Graphs
Unsupervised GNN model learns local updates for approximate MaxIS on dynamic graphs, achieving competitive ratios on 200-1000 node instances and 1.00-1.18x larger solutions than other unsupervised models when generalizing to 100x larger graphs.
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Graph Retention Networks for Dynamic Graphs
Graph Retention Networks extend retention to dynamic graphs to enable parallelizable training, O(1) inference, and chunkwise long-term training while delivering competitive performance with major efficiency gains.
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Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets
An entity-graph MARL framework (RACHE) using R-GCN message passing and attention pooling over train-service nodes outperforms baseline algorithms in railway pricing revenue across two simulated market scenarios.
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Explaining Temporal Graph Neural Networks via Feature-induced Information Flow
Event Relevance, built on modularized Normalized Relevance Measure, attributes ETGNN predictions to events by quantifying the entire event-induced information flow, including messages, and outperforms prior explainers on synthetic and real temporal graphs.
-
OPD-Evolver: Cultivating Holistic Agent Evolver via On-Policy Distillation
OPD-Evolver uses on-policy self-distillation in fast interaction and slow attribution loops to build agents with holistic memory competence, outperforming prior systems by up to 11.5% and allowing a 9B model to compete with much larger ones.
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Explainable Forecasting of Scientific Breakthroughs from Concept Network Dynamics
A two-stage LightGBM model on 59 features from concept networks forecasts link formation and intensity with ROC-AUC 0.95-0.967 across domains.
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SA-DTS: Semantic-Aware Digital Twin Synchronization over 6G Networks
SA-DTS achieves up to 94% bandwidth savings and 87% lower latency in digital twin synchronization by transmitting semantic features and reconstructing states with a partitioned knowledge graph.
-
Temporal Motif Signatures for Temporal Graph Neural Networks
A 13-coordinate leakage-safe motif feature map derived from three empirical axes of temporal motif activity improves TGNN performance on link prediction and edge classification across multiple real and synthetic datasets.
-
COPF: An Online Framework for Deployment-Stable Counterfactual Fairness in Evolving Graphs
COPF is a decision-layer framework for deployment-stable counterfactual fairness in online link recommendation on evolving graphs, using exposure counterfactuals, propensity logging, residual outcome indistinguishability, and graph-aware doubly robust estimators, supported by a noisy transfer theore
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Forget Less, Generalize More: Unifying Temporal and Structural Adaptation for Dynamic Graphs
DSRD unifies temporal and structural adaptation for dynamic graphs via a single recurrent retentive state with learnable time-sensitivity parameters in the decay kernels.
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Dynamic Link Prediction with Temporally Enhanced Signed Graph Neural Networks
Introduces HCIM framework with recency weighting, LSTM trajectories, and temporal attention to enhance static signed GNNs, showing statistically significant gains on Bitcoin, Reddit, and synthetic TSN datasets.
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Attention Dispersion in Dynamic Graph Transformers: Diagnosis and a Transferable Fix
Diagnoses attention dispersion in CTDG Transformers under temporal shift and introduces differential attention to suppress common signals and achieve SOTA on shifted benchmarks.
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Predicting Channel Closures in the Lightning Network with Machine Learning
Using two years of Lightning Network gossip data, a simple MLP predicts channel closure type (open/mutual/forced) with 0.38 macro F1, outperforming graph-based models.
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FAME: Forecasting Academic Impact via Continuous-Time Manifold Evolution
FAME models scientific topic trajectories in continuous time to forecast paper impact more accurately than LLMs by aligning manuscripts with field momentum in a dynamic latent space.
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PRISM: Iterative Cross-Modal Posterior Refinement for Dynamic Text-Attributed Graphs
PRISM iteratively transforms semantic priors into behavior-conditioned posteriors via cross-modal refinement to improve representation learning on dynamic text-attributed graphs.
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Knowledge Is Not Static: Order-Aware Hypergraph RAG for Language Models
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BiTA: Bidirectional Gated Recurrent Unit-Transformer Aggregator in a Temporal Graph Network Framework for Alert Prediction in Computer Networks
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LLM multi-agent systems augmented with data-driven event triggers and Hawkes processes simulate both micro-level interactions and macroscopic topologies in dynamic email networks for realistic phishing synthesis.
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Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space
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Bridging the Semantic-Collaborative Gap: An Asymmetric Graph Architecture for Cold-Start Item Recommendation
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Temporal Hyperbolic Graph Representation Learning for Scale-Free Internet Routing and Delay Prediction
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A2QTGN: Adaptive Amplitude Quantum-Integrated Temporal Graph Network for Dynamic Link Prediction
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Rethinking Fraud Safety Evaluation: Multi-Round Attacks Reveal Safety-Utility Tradeoffs in Graph-Context LLM Defenders
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