42 citations · 85 across the 20 of their papers we have counts for
10 papers · 1 filter
One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs
Jingzhe Liu, Haitao Mao, Zhikai Chen +6
Graph Neural Networks (GNNs) have emerged as a powerful tool to capture intricate network patterns, achieving success across different domains. However, existing GNNs require caref…
Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks
Rui Xue, Tong Zhao, Neil Shah +1
Graph neural networks (GNNs) have demonstrated remarkable success in graph representation learning, and various sampling approaches have been proposed to scale GNNs to applications…
Understanding and Scaling Collaborative Filtering Optimization from the Perspective of Matrix Rank
Donald Loveland, Xinyi Wu, Tong Zhao +3
Collaborative Filtering (CF) methods dominate real-world recommender systems given their ability to learn high-quality, sparse ID-embedding tables that effectively capture user pre…
Mosaic of Modalities: A Comprehensive Benchmark for Multimodal Graph Learning
Jing Zhu, Yuhang Zhou, Shengyi Qian +4
Graph machine learning has made significant strides in recent years, yet the integration of visual information with graph structure and its potential for improving performance in d…
How Does Message Passing Improve Collaborative Filtering?
Mingxuan Ju, William Shiao, Zhichun Guo +4
Collaborative filtering (CF) has exhibited prominent results for recommender systems and been broadly utilized for real-world applications. A branch of research enhances CF methods…
Improving Out-of-Vocabulary Handling in Recommendation Systems
William Shiao, Mingxuan Ju, Zhichun Guo +5
Recommendation systems (RS) are an increasingly relevant area for both academic and industry researchers, given their widespread impact on the daily online experiences of billions…
Node Duplication Improves Cold-start Link Prediction
Zhichun Guo, Tong Zhao, Yozen Liu +5
Graph Neural Networks (GNNs) are prominent in graph machine learning and have shown state-of-the-art performance in Link Prediction (LP) tasks. Nonetheless, recent studies show tha…
LLaGA: Large Language and Graph Assistant
Runjin Chen, Tong Zhao, Ajay Jaiswal +2
Graph Neural Networks (GNNs) have empowered the advance in graph-structured data analysis. Recently, the rise of Large Language Models (LLMs) like GPT-4 has heralded a new era in d…
Position: Graph Foundation Models are Already Here
Haitao Mao, Zhikai Chen, Wenzhuo Tang +6
Graph Foundation Models (GFMs) are emerging as a significant research topic in the graph domain, aiming to develop graph models trained on extensive and diverse data to enhance the…
Towards Neural Scaling Laws on Graphs
Jingzhe Liu, Haitao Mao, Zhikai Chen +3
Deep graph models (e.g., graph neural networks and graph transformers) have become important techniques for leveraging knowledge across various types of graphs. Yet, the neural sca…