103 citations · 430 across the 38 of their papers we have counts for
11 papers · 1 filter
Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks
Ujun Jeong, Kaize Ding, Lu Cheng +3
Nowadays, fake news easily propagates through online social networks and becomes a grand threat to individuals and society. Assessing the authenticity of news is challenging due to…
Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification
Zhen Tan, Song Wang, Kaize Ding +2
Few-shot node classification is tasked to provide accurate predictions for nodes from novel classes with only few representative labeled nodes. This problem has drawn tremendous at…
GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection
Yixin Liu, Kaize Ding, Huan Liu +1
Most existing deep learning models are trained based on the closed-world assumption, where the test data is assumed to be drawn i.i.d. from the same distribution as the training da…
Toward Robust Graph Semi-Supervised Learning against Extreme Data Scarcity
Kaize Ding, Elnaz Nouri, Guoqing Zheng +2
The success of graph neural networks on graph-based web mining highly relies on abundant human-annotated data, which is laborious to obtain in practice. When only few labeled nodes…
Task-Adaptive Few-shot Node Classification
Song Wang, Kaize Ding, Chuxu Zhang +2
Node classification is of great importance among various graph mining tasks. In practice, real-world graphs generally follow the long-tail distribution, where a large number of cla…
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…