42 citations · 74 across the 16 of their papers we have counts for
12 papers · 1 filter
TGTOD: A Global Temporal Graph Transformer for Outlier Detection at Scale
Kay Liu, Jiahao Ding, MohamadAli Torkamani +1
While Transformers have revolutionized machine learning on various data, existing Transformers for temporal graphs face limitations in (1) restricted receptive fields, (2) overhead…
BANGS: Game-Theoretic Node Selection for Graph Self-Training
Fangxin Wang, Kay Liu, Sourav Medya +1
Graph self-training is a semi-supervised learning method that iteratively selects a set of unlabeled data to retrain the underlying graph neural network (GNN) model and improve its…
LEGO-Learn: Label-Efficient Graph Open-Set Learning
Haoyan Xu, Kay Liu, Zhengtao Yao +4
How can we train graph-based models to recognize unseen classes while keeping labeling costs low? Graph open-set learning (GOL) and out-of-distribution (OOD) detection aim to addre…
FedGraph: A Research Library and Benchmark for Federated Graph Learning
Yuhang Yao, Yuan Li, Xinyi Fan +7
Federated graph learning is an emerging field with significant practical challenges. While algorithms have been proposed to improve the accuracy of training graph neural networks,…
Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement
Wenjing Chang, Kay Liu, Philip S. Yu +1
Graph anomaly detection (GAD) is increasingly crucial in various applications, ranging from financial fraud detection to fake news detection. However, current GAD methods largely o…
Uncertainty in Graph Neural Networks: A Survey
Fangxin Wang, Yuqing Liu, Kay Liu +3
Graph Neural Networks (GNNs) have been extensively used in various real-world applications. However, the predictive uncertainty of GNNs stemming from diverse sources such as inhere…
Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks
Qian Ma, Hongliang Chi, Hengrui Zhang +6
The rise of self-supervised learning, which operates without the need for labeled data, has garnered significant interest within the graph learning community. This enthusiasm has l…
Multitask Active Learning for Graph Anomaly Detection
Wenjing Chang, Kay Liu, Kaize Ding +2
In the web era, graph machine learning has been widely used on ubiquitous graph-structured data. As a pivotal component for bolstering web security and enhancing the robustness of…
Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Models
Kay Liu, Hengrui Zhang, Ziqing Hu +2
A fundamental challenge confronting supervised graph outlier detection algorithms is the prevalent problem of class imbalance, where the scarcity of outlier instances compared to n…
Equal Opportunity of Coverage in Fair Regression
Fangxin Wang, Lu Cheng, Ruocheng Guo +2
We study fair machine learning (ML) under predictive uncertainty to enable reliable and trustworthy decision-making. The seminal work of ``equalized coverage'' proposed an uncertai…
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs
Kay Liu, Yingtong Dou, Yue Zhao +12
Detecting which nodes in graphs are outliers is a relatively new machine learning task with numerous applications. Despite the proliferation of algorithms developed in recent years…
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…