9 citations · 14 across the 5 of their papers we have counts for
5 papers
Scalable and Adaptive Spectral Embedding for Attributed Graph Clustering
Yunhui Liu, Tieke He, Qing Wu +2
Attributed graph clustering, which aims to group the nodes of an attributed graph into disjoint clusters, has made promising advancements in recent years. However, most existing me…
Bootstrap Latents of Nodes and Neighbors for Graph Self-Supervised Learning
Yunhui Liu, Huaisong Zhang, Tieke He +2
Contrastive learning is a significant paradigm in graph self-supervised learning. However, it requires negative samples to prevent model collapse and learn discriminative represent…
ROIC-DM: Robust Text Inference and Classification via Diffusion Model
Shilong Yuan, Wei Yuan, Hongzhi Yin +1
While language models have made many milestones in text inference and classification tasks, they remain susceptible to adversarial attacks that can lead to unforeseen outcomes. Exi…
Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures
Wei Yuan, Quoc Viet Hung Nguyen, Tieke He +2
Federated Recommender Systems (FedRecs) are considered privacy-preserving techniques to collaboratively learn a recommendation model without sharing user data. Since all participan…
Interaction-level Membership Inference Attack Against Federated Recommender Systems
Wei Yuan, Chaoqun Yang, Quoc Viet Hung Nguyen +3
The marriage of federated learning and recommender system (FedRec) has been widely used to address the growing data privacy concerns in personalized recommendation services. In Fed…