29 citations · 49 across the 12 of their papers we have counts for
12 papers
Rethinking Large Language Model Architectures for Sequential Recommendations
Hanbing Wang, Xiaorui Liu, Wenqi Fan +7
Recently, sequential recommendation has been adapted to the LLM paradigm to enjoy the power of LLMs. LLM-based methods usually formulate recommendation information into natural lan…
Automated Polynomial Filter Learning for Graph Neural Networks
Wendi Yu, Zhichao Hou, Xiaorui Liu
Polynomial graph filters have been widely used as guiding principles in the design of Graph Neural Networks (GNNs). Recently, the adaptive learning of the polynomial graph filters…
Harnessing Trust in Directed Graphs: Redefining Robustness of Graph Learning
Zhichao Hou, Xitong Zhang, Wei Wang +2
Existing research on robust Graph Neural Networks (GNNs) focuses predominantly on undirected graphs, overlooking the trustworthiness inherent in the link directions of directed gra…
Towards Label Position Bias in Graph Neural Networks
Haoyu Han, Xiaorui Liu, Feng Shi +3
Graph Neural Networks (GNNs) have emerged as a powerful tool for semi-supervised node classification tasks. However, recent studies have revealed various biases in GNNs stemming fr…
LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation
Rui Xue, Haoyu Han, MohamadAli Torkamani +2
Recent works have demonstrated the benefits of capturing long-distance dependency in graphs by deeper graph neural networks (GNNs). But deeper GNNs suffer from the long-lasting sca…
Towards Fair Classification against Poisoning Attacks
Han Xu, Xiaorui Liu, Yuxuan Wan +1
Fair classification aims to stress the classification models to achieve the equality (treatment or prediction quality) among different sensitive groups. However, fair classificatio…
Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective
Wei Jin, Xiaorui Liu, Yao Ma +2
Recent years have witnessed remarkable success achieved by graph neural networks (GNNs) in many real-world applications such as recommendation and drug discovery. Despite the succe…
Alternately Optimized Graph Neural Networks
Haoyu Han, Xiaorui Liu, Haitao Mao +4
Graph Neural Networks (GNNs) have greatly advanced the semi-supervised node classification task on graphs. The majority of existing GNNs are trained in an end-to-end manner that ca…
Defense Against Gradient Leakage Attacks via Learning to Obscure Data
Yuxuan Wan, Han Xu, Xiaorui Liu +3
Federated learning is considered as an effective privacy-preserving learning mechanism that separates the client's data and model training process. However, federated learning is s…
Decentralized Composite Optimization with Compression
Yao Li, Xiaorui Liu, Jiliang Tang +2
Decentralized optimization and communication compression have exhibited their great potential in accelerating distributed machine learning by mitigating the communication bottlenec…
Graph Trend Filtering Networks for Recommendations
Wenqi Fan, Xiaorui Liu, Wei Jin +3
Recommender systems aim to provide personalized services to users and are playing an increasingly important role in our daily lives. The key of recommender systems is to predict ho…
Jointly Attacking Graph Neural Network and its Explanations
Wenqi Fan, Wei Jin, Xiaorui Liu +7
Graph Neural Networks (GNNs) have boosted the performance for many graph-related tasks. Despite the great success, recent studies have shown that GNNs are highly vulnerable to adve…