214 citations · 233 across the 6 of their papers we have counts for
6 papers
Correlation-Aware Graph Convolutional Networks for Multi-Label Node Classification
Yuanchen Bei, Weizhi Chen, Hao Chen +5
Multi-label node classification is an important yet under-explored domain in graph mining as many real-world nodes belong to multiple categories rather than just a single one. Alth…
Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation
Baoyu Jing, Dawei Zhou, Kan Ren +1
Spatiotemporal time series are usually collected via monitoring sensors placed at different locations, which usually contain missing values due to various failures, such as mechani…
Contrastive Unlearning: A Contrastive Approach to Machine Unlearning
Hong kyu Lee, Qiuchen Zhang, Carl Yang +2
Machine unlearning aims to eliminate the influence of a subset of training samples (i.e., unlearning samples) from a trained model. Effectively and efficiently removing the unlearn…
BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning
Junru Chen, Yang Yang, Tao Yu +3
Epilepsy is one of the most serious neurological diseases, affecting 1-2% of the world's population. The diagnosis of epilepsy depends heavily on the recognition of epileptic waves…
Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications
Han Xie, Da Zheng, Jun Ma +9
Model pre-training on large text corpora has been demonstrated effective for various downstream applications in the NLP domain. In the graph mining domain, a similar analogy can be…
BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks
Hejie Cui, Wei Dai, Yanqiao Zhu +7
Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neur…