4 citations · 7 across the 7 of their papers we have counts for
5 papers · 1 filter
Raising the Bar in Graph OOD Generalization: Invariant Learning Beyond Explicit Environment Modeling
Xu Shen, Yixin Liu, Yili Wang +5
Out-of-distribution (OOD) generalization has emerged as a critical challenge in graph learning, as real-world graph data often exhibit diverse and shifting environments that tradit…
Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space
Xin He, Yili Wang, Wenqi Fan +4
Graph Neural Networks (GNNs) have shown great success in various graph-based learning tasks. However, it often faces the issue of over-smoothing as the model depth increases, which…
Graph Defense Diffusion Model
Xin He, Wenqi Fan, Yili Wang +4
Graph Neural Networks (GNNs) are highly vulnerable to adversarial attacks, which can greatly degrade their performance. Existing graph purification methods attempt to address this…
Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark
Yili Wang, Yixin Liu, Xu Shen +6
To build safe and reliable graph machine learning systems, unsupervised graph-level anomaly detection (GLAD) and unsupervised graph-level out-of-distribution (OOD) detection (GLOD)…
Rethinking Independent Cross-Entropy Loss For Graph-Structured Data
Rui Miao, Kaixiong Zhou, Yili Wang +3
Graph neural networks (GNNs) have exhibited prominent performance in learning graph-structured data. Considering node classification task, based on the i.i.d assumption among node…