5 papers
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…
GRIP: In-Parameter Graph Reasoning through Fine-Tuning Large Language Models
Jiarui Feng, Donghong Cai, Yixin Chen +1
Large Language Models (LLMs) have demonstrated remarkable capabilities in modeling sequential textual data and generalizing across diverse tasks. However, effectively adapting LLMs…
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…