Xiang Song

DGL-KE: Training Knowledge Graph Embeddings at Scale

2020 · arXiv (Cornell University)

Knowledge graphs have emerged as a key abstraction for organizing information in diverse domains and their embeddings are increasingly used to harness their information in various information retrieval and machine learning tasks. However, the ever …

Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks

2019 · arXiv (Cornell University)

Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs. In this paper, we present the design principles and implementation of Deep Graph Library (DGL). DGL …

IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research

2023 · arXiv (Cornell University)

Graph neural networks (GNNs) have shown high potential for a variety of real-world, challenging applications, but one of the major obstacles in GNN research is the lack of large-scale flexible datasets. Most existing public datasets …

Parameter-Efficient Tuning Large Language Models for Graph Representation Learning

2024 · arXiv (Cornell University)

Text-rich graphs, which exhibit rich textual information on nodes and edges, are prevalent across a wide range of real-world business applications. Large Language Models (LLMs) have demonstrated remarkable abilities in understanding text, which also introduced …