arXiv Machine Learning
GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Jul 21
A Survey on GNN-based Link Prediction: Techniques, Applications, and Challenges
arXiv:2607. 16198v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have emerged as the leading paradigm for link prediction, enabling the inference of missing connections and the anticipation of potential future links.
arXiv AI
6d ago
Bridging Semantics and Structure: A Theoretically Grounded Framework for Link Prediction on TAGs
Bridge-LP is a new framework for link prediction on text‑attributed graphs that combines Graph Neural Networks and Language Models. It introduces three design components: jumping connections for robust language model training, compressed embeddings to encode neighborhood information, and a cache embedding strategy to reduce computational cost. Experiments on nine datasets show that Bridge‑LP outperforms 40 existing models, achieving up to a 43.06% improvement in Mean Reciprocal Rank.
arXiv Machine Learning
Aug 28
SLM-Conditioned Hierarchical Relation Routing for Labeled Property Graph Learning
The paper introduces SLM-Conditioned Hierarchical Relation Routing, a graph neural network architecture that incorporates a small language model to guide message selection in labeled property graphs. It combines structural states, node and relationship property encodings, and relationship types into messages, then uses a parameter‑efficient language model to generate target‑conditioned routing queries that filter relevant messages and route information across relation‑level summaries. The resulting representation updates a topology anchor with bounded residuals, preserving structural evidence while allowing contextual semantic information to influence predictions, and offers interpretable analysis at both neighbor and relationship‑type levels.
arXiv Machine Learning
Jun 25
A Hybrid TGN-SEAL Model for Dynamic Graph Link Prediction
arXiv:2602. 14239v3 Announce Type: replace-cross Abstract: Predicting links in sparse, continuously evolving networks is a central challenge in network science.
arXiv AI
Jul 28
Plain Transformers are Surprisingly Powerful Link Predictors
arXiv:2602. 01553v3 Announce Type: replace-cross Abstract: Link prediction is a core challenge in graph machine learning, demanding models that capture rich and complex topological dependencies.
arXiv Machine Learning
Sep 22
SiST-GNN: Simultaneous Spatial-Temporal Message Passing for Dynamic Graph Representation Learning
SiST‑GNN introduces a simultaneous spatial‑temporal message‑passing framework for dynamic graph neural networks, fusing per‑node temporal embeddings with spatial aggregation in a single operation. By maintaining a recurrent hidden state per node and treating it as a cross‑time edge, the model jointly reasons over topology and evolution. Experiments on link‑prediction and node‑classification benchmarks show significant improvements over prior methods, achieving up to 158% gains in live‑update link prediction and outperforming discrete‑time baselines by 7–23% in dynamic node classification.