111 citations · 717 across the 33 of their papers we have counts for
33 papers
Sub-graph Based Diffusion Model for Link Prediction
Hang Li, Wei Jin, Geri Skenderi +4
Denoising Diffusion Probabilistic Models (DDPMs) represent a contemporary class of generative models with exceptional qualities in both synthesis and maximizing the data likelihood…
Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustness
Kai Guo, Zewen Liu, Zhikai Chen +4
Large Language Models (LLMs) have demonstrated remarkable performance across various natural language processing tasks. Recently, several LLMs-based pipelines have been developed t…
Enhancing ID and Text Fusion via Alternative Training in Session-based Recommendation
Juanhui Li, Haoyu Han, Zhikai Chen +4
Session-based recommendation has gained increasing attention in recent years, with its aim to offer tailored suggestions based on users' historical behaviors within sessions. To ad…
Investigating Out-of-Distribution Generalization of GNNs: An Architecture Perspective
Kai Guo, Hongzhi Wen, Wei Jin +3
Graph neural networks (GNNs) have exhibited remarkable performance under the assumption that test data comes from the same distribution of training data. However, in real-world sce…
Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
Wei Jin, Haitao Mao, Zheng Li +17
Modeling customer shopping intentions is a crucial task for e-commerce, as it directly impacts user experience and engagement. Thus, accurately understanding customer preferences i…
Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs
Zhikai Chen, Haitao Mao, Hang Li +8
Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily…