Kaize Ding

BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs

2022 · arXiv (Cornell University)

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 for this task, there has been no standard comprehensive …

Beyond Generalization: A Survey of Out-Of-Distribution Adaptation on Graphs

2024 · arXiv (Cornell University)

Distribution shifts on graphs -- the data distribution discrepancies between training and testing a graph machine learning model, are often ubiquitous and unavoidable in real-world scenarios. Such shifts may severely deteriorate the performance of the …

Data-Efficient Graph Learning

2024 · Proceedings of the AAAI Conference on Artificial Intelligence

My research strives to develop fundamental graph-centric learning algorithms to reduce the need for human supervision in low-resource scenarios. The focus is on achieving effective and reliable data-efficient learning on graphs, which can be summarized …

LEGO-Learn: Label-Efficient Graph Open-Set Learning

2024 · arXiv (Cornell University)

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 address this challenge by training models that can accurately classify …