5 papers
Inductive Subgraphs as Shortcuts: Causal Disentanglement for Heterophilic Graph Learning
Xiangmeng Wang, Qian Li, Haiyang Xia +3
Heterophily is a prevalent property of real-world graphs and is well known to impair the performance of homophilic Graph Neural Networks (GNNs). Prior work has attempted to adapt G…
Debiasing Sequential Recommendation with Time-aware Inverse Propensity Scoring
Sirui Huang, Jing Long, Qian Li +2
Sequential Recommendation (SR) predicts users next interactions by modeling the temporal order of their historical behaviors. Existing approaches, including traditional sequential…
HyperG: Hypergraph-Enhanced LLMs for Structured Knowledge
Sirui Huang, Hanqian Li, Yanggan Gu +3
Given that substantial amounts of domain-specific knowledge are stored in structured formats, such as web data organized through HTML, Large Language Models (LLMs) are expected to…
LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning
Haoran Yang, Xiangyu Zhao, Sirui Huang +2
Graph Contrastive Learning (GCL) is a potent paradigm for self-supervised graph learning that has attracted attention across various application scenarios. However, GCL for learnin…
Reasoning Factual Knowledge in Structured Data with Large Language Models
Sirui Huang, Yanggan Gu, Xuming Hu +3
Large language models (LLMs) have made remarkable progress in various natural language processing tasks as a benefit of their capability to comprehend and reason with factual knowl…