4 papers · 1 filter
Uncertainty-Aware Robust Learning on Noisy Graphs
Shuyi Chen, Kaize Ding, Shixiang Zhu
Graph neural networks (GNNs) have excelled in various graph learning tasks, particularly node classification. However, their performance is often hampered by noisy measurements in…
MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detection
Xiongxiao Xu, Kaize Ding, Canyu Chen +1
Graph anomaly detection has long been an important problem in various domains pertaining to information security such as financial fraud, social spam and network intrusion. The maj…
PyGOD: A Python Library for Graph Outlier Detection
Kay Liu, Yingtong Dou, Xueying Ding +5
PyGOD is an open-source Python library for detecting outliers in graph data. As the first comprehensive library of its kind, PyGOD supports a wide array of leading graph-based meth…
Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization
Haohui Wang, Baoyu Jing, Kaize Ding +6
In the context of long-tail classification on graphs, the vast majority of existing work primarily revolves around the development of model debiasing strategies, intending to mitig…