8 citations · 17 across the 7 of their papers we have counts for
6 papers · 1 filter
RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databases
Jinyu Yang, Cheng Yang, Junze Chen +4
Relational databases (RDBs) remain the cornerstone of modern data systems and support diverse predictive tasks. Recent relational deep learning (RDL) methods enable end-to-end pred…
GOFA: A Generative One-For-All Model for Joint Graph Language Modeling
Lecheng Kong, Jiarui Feng, Hao Liu +4
Foundation models, such as Large Language Models (LLMs) or Large Vision Models (LVMs), have emerged as one of the most powerful tools in the respective fields. However, unlike text…
TAGLAS: An atlas of text-attributed graph datasets in the era of large graph and language models
Jiarui Feng, Hao Liu, Lecheng Kong +3
In this report, we present TAGLAS, an atlas of text-attributed graph (TAG) datasets and benchmarks. TAGs are graphs with node and edge features represented in text, which have rece…
SPGNN: Recognizing Salient Subgraph Patterns via Enhanced Graph Convolution and Pooling
Zehao Dong, Muhan Zhang, Yixin Chen
Graph neural networks (GNNs) have revolutionized the field of machine learning on non-Euclidean data such as graphs and networks. GNNs effectively implement node representation lea…
MAG-GNN: Reinforcement Learning Boosted Graph Neural Network
Lecheng Kong, Jiarui Feng, Hao Liu +3
While Graph Neural Networks (GNNs) recently became powerful tools in graph learning tasks, considerable efforts have been spent on improving GNNs' structural encoding ability. A pa…
One for All: Towards Training One Graph Model for All Classification Tasks
Hao Liu, Jiarui Feng, Lecheng Kong +4
Designing a single model to address multiple tasks has been a long-standing objective in artificial intelligence. Recently, large language models have demonstrated exceptional capa…