Copertina

Enhancing the Expressivity of Temporal Graph Networks through Source-Target Identification

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Tjandra, Benedict Aaron, Barbero, Federico, Bronstein, Michael
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

T-GRAB: A Synthetic Diagnostic Benchmark for Learning on Temporal Graphs
di: Dizaji, Alireza, et al.
Pubblicazione: (2025)

Bundle Neural Networks for message diffusion on graphs
di: Bamberger, Jacob, et al.
Pubblicazione: (2024)

Locality-Aware Graph-Rewiring in GNNs
di: Barbero, Federico, et al.
Pubblicazione: (2023)

Fine-Tuning Large Language Models to Appropriately Abstain with Semantic Entropy
di: Tjandra, Benedict Aaron, et al.
Pubblicazione: (2024)

How Expressive are Knowledge Graph Foundation Models?
di: Huang, Xingyue, et al.
Pubblicazione: (2025)

On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning
di: Arroyo, Álvaro, et al.
Pubblicazione: (2025)

Effective Sample Size and Generalization Bounds for Temporal Networks
di: Gahtan, Barak, et al.
Pubblicazione: (2025)

Reducing Smoothness with Expressive Memory Enhanced Hierarchical Graph Neural Networks
di: Bailie, Thomas, et al.
Pubblicazione: (2025)

Expressive Power of Temporal Message Passing
di: Wałęga, Przemysław Andrzej, et al.
Pubblicazione: (2024)

Random Search Neural Networks for Efficient and Expressive Graph Learning
di: Ito, Michael, et al.
Pubblicazione: (2025)

Scalable Expressiveness through Preprocessed Graph Perturbations
di: Saber, Danial, et al.
Pubblicazione: (2024)

Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting
di: Jin, Ming, et al.
Pubblicazione: (2023)

Cooperative Graph Neural Networks
di: Finkelshtein, Ben, et al.
Pubblicazione: (2023)

Homomorphism Counts for Graph Neural Networks: All About That Basis
di: Jin, Emily, et al.
Pubblicazione: (2024)

Over-squashing in Spatiotemporal Graph Neural Networks
di: Marisca, Ivan, et al.
Pubblicazione: (2025)

Generalization, Expressivity, and Universality of Graph Neural Networks on Attributed Graphs
di: Rauchwerger, Levi, et al.
Pubblicazione: (2024)

Graph Neural Networks Are Not Continuous Across Graph Resolutions
di: Koke, Christian, et al.
Pubblicazione: (2026)

Graph Kernel Neural Networks
di: Cosmo, Luca, et al.
Pubblicazione: (2021)

On the Expressive Power of Graph Neural Networks
di: Nalwade, Ashwin, et al.
Pubblicazione: (2024)

Towards Bridging Generalization and Expressivity of Graph Neural Networks
di: Li, Shouheng, et al.
Pubblicazione: (2024)

Homomorphism Expressivity of Spectral Invariant Graph Neural Networks
di: Gai, Jingchu, et al.
Pubblicazione: (2025)

Invariant-Stratified Propagation for Expressive Graph Neural Networks
di: Hevapathige, Asela, et al.
Pubblicazione: (2026)

On the Relationship Between Robustness and Expressivity of Graph Neural Networks
di: Kummer, Lorenz, et al.
Pubblicazione: (2025)

Full-Spectrum Graph Neural Networks: Expressive and Scalable
di: Wang, Xiaohan, et al.
Pubblicazione: (2026)

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles
di: Chen, Ziang, et al.
Pubblicazione: (2025)

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality
di: Southern, Joshua, et al.
Pubblicazione: (2025)

Topological Blindspots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity
di: Eitan, Yam, et al.
Pubblicazione: (2024)

Are Large Language Models Good Temporal Graph Learners?
di: Huang, Shenyang, et al.
Pubblicazione: (2025)

On the Expressive Power of Geometric Graph Neural Networks
di: Joshi, Chaitanya K., et al.
Pubblicazione: (2023)

Pre-Training Identification of Graph Winning Tickets in Adaptive Spatial-Temporal Graph Neural Networks
di: Duan, Wenying, et al.
Pubblicazione: (2024)

SuperMAN: Interpretable and Expressive Networks over Temporally Sparse Heterogeneous Data
di: Bechler-Speicher, Maya, et al.
Pubblicazione: (2025)

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals
di: Sripathmanathan, Baskaran, et al.
Pubblicazione: (2025)

On Measuring Long-Range Interactions in Graph Neural Networks
di: Bamberger, Jacob, et al.
Pubblicazione: (2025)

Weisfeiler and Leman Follow the Arrow of Time: Expressive Power of Message Passing in Temporal Event Graphs
di: Heeg, Franziska, et al.
Pubblicazione: (2025)

Scalable Message Passing Neural Networks: No Need for Attention in Large Graph Representation Learning
di: Borde, Haitz Sáez de Ocáriz, et al.
Pubblicazione: (2024)

CombAlign: Enhancing Model Expressiveness in Unsupervised Graph Alignment
di: Chen, Songyang, et al.
Pubblicazione: (2024)

Supercharging Graph Transformers with Advective Diffusion
di: Wu, Qitian, et al.
Pubblicazione: (2023)

The Expressive Power of Graph Neural Networks: A Survey
di: Zhang, Bingxu, et al.
Pubblicazione: (2023)

Towards Dynamic Graph Neural Networks with Provably High-Order Expressive Power
di: Wang, Zhe, et al.
Pubblicazione: (2024)

Boosting Graph Neural Network Expressivity with Learnable Lanczos Constraints
di: Azizi, Niloofar, et al.
Pubblicazione: (2024)