5 papers
Simplifying Graph Kernels for Efficient
Lin Wang, Shijie Wang, Sirui Huang +1
While kernel methods and Graph Neural Networks offer complementary strengths, integrating the two has posed challenges in efficiency and scalability. The Graph Neural Tangent Kerne…
Contrastive Learning Augmented Social Recommendations
Lin Wang, Weisong Wang, Xuanji Xiao +1
Recommender systems are essential for modern content platforms, yet traditional behavior-based models often struggle with cold users who have limited interaction data. Engaging the…
Efficient Graph Condensation via Gaussian Process
Lin Wang, Qing Li
Graph condensation reduces the size of large graphs while preserving performance, addressing the scalability challenges of Graph Neural Networks caused by computational inefficienc…
Graph Unlearning with Efficient Partial Retraining
Jiahao Zhang, Lin Wang, Shijie Wang +1
Graph Neural Networks (GNNs) have achieved remarkable success in various real-world applications. However, GNNs may be trained on undesirable graph data, which can degrade their pe…
Fast Graph Condensation with Structure-based Neural Tangent Kernel
Lin Wang, Wenqi Fan, Jiatong Li +2
The rapid development of Internet technology has given rise to a vast amount of graph-structured data. Graph Neural Networks (GNNs), as an effective method for various graph mining…