7 papers
Disentangled Instrumental Variables for Causal Inference with Networked Observational Data
Zhirong Huang, Debo Cheng, Guixian Zhang +3
Instrumental variables (IVs) are crucial for addressing unobservable confounders, yet their stringent exogeneity assumptions pose significant challenges in networked data. Existing…
Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation
Chengyu Li, Debo Cheng, Guixian Zhang +2
Graph Neural Networks (GNNs) have demonstrated strong performance in graph representation learning across various real-world applications. However, they often produce biased predic…
Mitigating Propensity Bias of Large Language Models for Recommender Systems
Guixian Zhang, Guan Yuan, Debo Cheng +3
The rapid development of Large Language Models (LLMs) creates new opportunities for recommender systems, especially by exploiting the side information (e.g., descriptions and analy…
Community-Centric Graph Unlearning
Yi Li, Shichao Zhang, Guixian Zhang +1
Graph unlearning technology has become increasingly important since the advent of the `right to be forgotten' and the growing concerns about the privacy and security of artificial…
Towards Fair Graph Representation Learning in Social Networks
Guixian Zhang, Guan Yuan, Debo Cheng +3
With the widespread use of Graph Neural Networks (GNNs) for representation learning from network data, the fairness of GNN models has raised great attention lately. Fair GNNs aim t…
Multi-Cause Deconfounding for Recommender Systems with Latent Confounders
Zhirong Huang, Shichao Zhang, Debo Cheng +3
In recommender systems, various latent confounding factors (e.g., user social environment and item public attractiveness) can affect user behavior, item exposure, and feedback in d…