257 citations · 1.2k across the 74 of their papers we have counts for
9 papers · 1 filter
Unveiling the Impact of Local Homophily on GNN Fairness: In-Depth Analysis and New Benchmarks
Donald Loveland, Danai Koutra
Graph Neural Networks (GNNs) often struggle to generalize when graphs exhibit both homophily (same-class connections) and heterophily (different-class connections). Specifically, G…
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…
GT2Vec: Large Language Models as Multi-Modal Encoders for Text and Graph-Structured Data
Jiacheng Lin, Kun Qian, Haoyu Han +9
Graph-structured information offers rich contextual information that can enhance language models by providing structured relationships and hierarchies, leading to more expressive e…
On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks
Jiong Zhu, Gaotang Li, Yao-An Yang +3
Heterophily, or the tendency of connected nodes in networks to have different class labels or dissimilar features, has been identified as challenging for many Graph Neural Network…
Large Generative Graph Models
Yu Wang, Ryan A. Rossi, Namyong Park +6
Large Generative Models (LGMs) such as GPT, Stable Diffusion, Sora, and Suno are trained on a huge amount of language corpus, images, videos, and audio that are extremely diverse f…
LinkGPT: Teaching Large Language Models To Predict Missing Links
Zhongmou He, Jing Zhu, Shengyi Qian +2
Large Language Models (LLMs) have shown promising results on various language and vision tasks. Recently, there has been growing interest in applying LLMs to graph-based tasks, par…