Image de couverture de livre

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Ferrini, Francesco, Lachi, Veronica, Longa, Antonio, Lepri, Bruno, Passerini, Andrea
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Bridging Theory and Practice in Link Representation with Graph Neural Networks
par: Lachi, Veronica, et autres
Publié: (2025)

Rethinking GNNs and Missing Features: Challenges, Evaluation and a Robust Solution
par: Ferrini, Francesco, et autres
Publié: (2026)

Boosting Relational Deep Learning with Pretrained Tabular Models
par: Lachi, Veronica, et autres
Publié: (2025)

A Self-Explainable Heterogeneous GNN for Relational Deep Learning
par: Ferrini, Francesco, et autres
Publié: (2024)

Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities
par: Longa, Antonio, et autres
Publié: (2023)

Reconsidering Faithfulness in Regular, Self-Explainable and Domain Invariant GNNs
par: Azzolin, Steve, et autres
Publié: (2024)

Explaining the Explainers in Graph Neural Networks: a Comparative Study
par: Longa, Antonio, et autres
Publié: (2022)

Simple Path Structural Encoding for Graph Transformers
par: Airale, Louis, et autres
Publié: (2025)

Time Can Invalidate Algorithmic Recourse
par: De Toni, Giovanni, et autres
Publié: (2024)

Actionable Real-Time Modeling of Surgical Team Dynamics via Time-Expanded Interaction Graphs
par: De Luca, Vincenzo Marco, et autres
Publié: (2026)

GNN Explanations that do not Explain and How to find Them
par: Azzolin, Steve, et autres
Publié: (2026)

Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective
par: Azzolin, Steve, et autres
Publié: (2025)

xAI-Drop: Don't Use What You Cannot Explain
par: De Luca, Vincenzo Marco, et autres
Publié: (2024)

Hyperdimensional Probe: Decoding LLM Representations via Vector Symbolic Architectures
par: Bronzini, Marco, et autres
Publié: (2025)

Personalized Algorithmic Recourse with Preference Elicitation
par: De Toni, Giovanni, et autres
Publié: (2022)

To Ask or Not to Ask: Learning to Require Human Feedback
par: Pugnana, Andrea, et autres
Publié: (2025)

On the Expressive Power of GNNs to Solve Linear SDPs
par: Qian, Chendi, et autres
Publié: (2026)

Expressivity and Generalization: Fragment-Biases for Molecular GNNs
par: Wollschläger, Tom, et autres
Publié: (2024)

On the Expressive Power of GNNs for Boolean Satisfiability
par: Peltonen, Saku, et autres
Publié: (2026)

PROXI: Challenging the GNNs for Link Prediction
par: Tola, Astrit, et autres
Publié: (2024)

Spectral Journey: How Transformers Predict the Shortest Path
par: Cohen, Andrew, et autres
Publié: (2025)

Flow Matters: Directional and Expressive GNNs for Heterophilic Graphs
par: Gupta, Arman, et autres
Publié: (2025)

Chordless Structure: A Pathway to Simple and Expressive GNNs
par: Pan, Hongxu, et autres
Publié: (2025)

Rethinking the Expressive Power of GNNs via Graph Biconnectivity
par: Zhang, Bohang, et autres
Publié: (2023)

Can GNNs Learn Link Heuristics? A Concise Review and Evaluation of Link Prediction Methods
par: Liang, Shuming, et autres
Publié: (2024)

Exploiting Preference Elicitation in Interactive and User-centered Algorithmic Recourse: An Initial Exploration
par: Esfahani, Seyedehdelaram, et autres
Publié: (2024)

What Expressivity Theory Misses: Message Passing Complexity for GNNs
par: Kemper, Niklas, et autres
Publié: (2025)

Message Passing on the Edge: Towards Scalable and Expressive GNNs
par: Barceló, Pablo, et autres
Publié: (2025)

Incremental Approximate Single-Source Shortest Paths with Predictions
par: McCauley, Samuel, et autres
Publié: (2025)

Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs
par: Beddar-Wiesing, Silvia, et autres
Publié: (2022)

Stochastic Shortest Path with Sparse Adversarial Costs
par: Johnson, Emmeran, et autres
Publié: (2025)

Unsupervised Learning for the Elementary Shortest Path Problem
par: Chen, Jingyi, et autres
Publié: (2025)

Learning Shortest Paths When Data is Scarce
par: Matsypura, Dmytro, et autres
Publié: (2026)

Expressivity-Efficiency Tradeoffs for Hybrid Sequence Models
par: Cooper, John, et autres
Publié: (2026)

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality
par: Southern, Joshua, et autres
Publié: (2025)

Towards Understanding the Expressive Power of GNNs with Global Readout
par: Funk, Maurice, et autres
Publié: (2026)

Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model
par: Tarbouriech, Jean, et autres
Publié: (2026)

A Boolean Function-Theoretic Framework for Expressivity in GNNs with Applications to Fair Graph Mining
par: Pal, Manjish
Publié: (2026)

Time Series Foundation Models are Flow Predictors
par: Luca, Massimiliano, et autres
Publié: (2025)

Skeleton-Guided Learning for Shortest Path Search
par: Liu, Tiantian, et autres
Publié: (2025)