4 papers · 1 filter
Graph homophily booster: Reimagining the role of discrete features in heterophilic graph learning
Ruizhong Qiu, Ting-Wei Li, Gaotang Li +1
Graph neural networks (GNNs) have emerged as a powerful tool for modeling graph-structured data. However, existing GNNs often struggle with heterophilic graphs, where connected nod…
Continual Low-Rank Adapters for LLM-based Generative Recommender Systems
Hyunsik Yoo, Ting-Wei Li, SeongKu Kang +4
While large language models (LLMs) achieve strong performance in recommendation, they face challenges in continual learning as users, items, and user preferences evolve over time.…
Graph Homophily Booster: Rethinking the Role of Discrete Features on Heterophilic Graphs
Ruizhong Qiu, Ting-Wei Li, Gaotang Li +1
Graph neural networks (GNNs) have emerged as a powerful tool for modeling graph-structured data. However, existing GNNs often struggle with heterophilic graphs, where connected nod…
Graph Data Selection for Domain Adaptation: A Model-Free Approach
Ting-Wei Li, Ruizhong Qiu, Hanghang Tong
Graph domain adaptation (GDA) is a fundamental task in graph machine learning, with techniques like shift-robust graph neural networks (GNNs) and specialized training procedures to…