8 citations · 17 across the 7 of their papers we have counts for
8 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…
Highly Accurate Disease Diagnosis and Highly Reproducible Biomarker Identification with PathFormer
Zehao Dong, Qihang Zhao, Philip R. O. Payne +6
Biomarker identification is critical for precise disease diagnosis and understanding disease pathogenesis in omics data analysis, like using fold change and regression analysis. Gr…
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…