arXiv AI
QUARTET: Quad-branch cross-Attention and Random-walk Traces for Enhancing Transformers on Relational Graphs
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
Hugging Face Trending Papers
Sep 22
QUARTET: Quad-branch cross-Attention and Random-walk Traces for Enhancing Transformers on Relational Graphs
Relational Deep Learning (RDL) models multi-table databases as heterogeneous temporal graphs, and graph transformers currently achieve state-of-the-art performance on benchmarks like RelBench. However...
arXiv AI
Jun 24
Invariant Graph Representations for Continuous-Time Dynamic Graphs Under Distribution Shifts
arXiv:2405. 19062v2 Announce Type: replace-cross Abstract: Continuous-Time Dynamic Graphs (CTDGs) enable fine-grained modeling of evolving relational systems.
arXiv Machine Learning
Jul 31
THGFM: Dual-Branch Temporal Heterogeneous Graph Fusion Model
arXiv:2607. 27303v1 Announce Type: new Abstract: Temporal heterogeneous graphs offer a natural abstraction for dynamic relational systems in which diverse node and relation types co-exist and evolve over time.
arXiv Machine Learning
Jun 30
GLIP: Graph and LLM Joint Pretraining for Graph-Level Tasks
arXiv:2606. 29773v1 Announce Type: new Abstract: Graphs are widely used to model relational systems, with applications in domains such as social networks, finance, and biomedicine.
Hugging Face Trending Papers
Jul 13
Surprisingly Simple and Effective Multi-Domain Graph Foundation Model through Graph-to-Table Alignment
Graph Foundation Models (GFMs) have emerged as a promising paradigm for learning transferable representations across diverse graph domains. Recent advancements in GFMs have been largely dominated by two paradigms: Graph Neural Network and Large Language Model (LLM) based methods.
arXiv Machine Learning
Aug 4
HP-JEPA: Hierarchical Partitioning for Multi-Resolution Graph Joint-Embedding Predictive Learning
arXiv:2608. 00491v1 Announce Type: new Abstract: Graph self-supervised learning aims to learn transferable representations from large-scale unlabeled graph data.