128 citations · 350 across the 37 of their papers we have counts for
27 papers · 1 filter
Can Subgraph Explanations Be Weaponized to Steal Graph Neural Networks?
Ojas Nimase, Jiate Li, Yue Zhao +1
Graph Machine Learning as a Service (GMLaaS) platforms increasingly implement explainability interfaces to meet regulatory transparency requirements. However, this transparency cre…
MolEdit: Knowledge Editing for Multimodal Molecule Language Models
Zhenyu Lei, Patrick Soga, Yaochen Zhu +3
Understanding and continuously refining multimodal molecular knowledge is crucial for advancing biomedicine, chemistry, and materials science. Molecule language models (MoLMs) have…
FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs
Zihan Chen, Xingbo Fu, Yushun Dong +2
Federated Graph Learning (FGL) empowers clients to collaboratively train Graph neural networks (GNNs) in a distributed manner while preserving data privacy. However, FGL methods us…
ST-FiT: Inductive Spatial-Temporal Forecasting with Limited Training Data
Zhenyu Lei, Yushun Dong, Jundong Li +1
Spatial-temporal graphs are widely used in a variety of real-world applications. Spatial-Temporal Graph Neural Networks (STGNNs) have emerged as a powerful tool to extract meaningf…
Graph Neural Networks Are More Than Filters: Revisiting and Benchmarking from A Spectral Perspective
Yushun Dong, Patrick Soga, Yinhan He +2
Graph Neural Networks (GNNs) have achieved remarkable success in various graph-based learning tasks. While their performance is often attributed to the powerful neighborhood aggreg…
Federated Graph Learning with Graphless Clients
Xingbo Fu, Song Wang, Yushun Dong +3
Federated Graph Learning (FGL) is tasked with training machine learning models, such as Graph Neural Networks (GNNs), for multiple clients, each with its own graph data. Existing m…
Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective for Molecular Property Prediction
Yinhan He, Zaiyi Zheng, Patrick Soga +3
In recent years, Graph Neural Networks (GNNs) have become successful in molecular property prediction tasks such as toxicity analysis. However, due to the black-box nature of GNNs,…
Towards Certified Unlearning for Deep Neural Networks
Binchi Zhang, Yushun Dong, Tianhao Wang +1
In the field of machine unlearning, certified unlearning has been extensively studied in convex machine learning models due to its high efficiency and strong theoretical guarantees…
IDEA: A Flexible Framework of Certified Unlearning for Graph Neural Networks
Yushun Dong, Binchi Zhang, Zhenyu Lei +2
Graph Neural Networks (GNNs) have been increasingly deployed in a plethora of applications. However, the graph data used for training may contain sensitive personal information of…
A Benchmark for Fairness-Aware Graph Learning
Yushun Dong, Song Wang, Zhenyu Lei +4
Fairness-aware graph learning has gained increasing attention in recent years. Nevertheless, there lacks a comprehensive benchmark to evaluate and compare different fairness-aware…
Rethinking Fair Graph Neural Networks from Re-balancing
Zhixun Li, Yushun Dong, Qiang Liu +1
Driven by the powerful representation ability of Graph Neural Networks (GNNs), plentiful GNN models have been widely deployed in many real-world applications. Nevertheless, due to…
Safety in Graph Machine Learning: Threats and Safeguards
Song Wang, Yushun Dong, Binchi Zhang +7
Graph Machine Learning (Graph ML) has witnessed substantial advancements in recent years. With their remarkable ability to process graph-structured data, Graph ML techniques have b…
Certified Defense on the Fairness of Graph Neural Networks
Yushun Dong, Binchi Zhang, Hanghang Tong +1
Graph Neural Networks (GNNs) have emerged as a prominent graph learning model in various graph-based tasks over the years. Nevertheless, due to the vulnerabilities of GNNs, it has…
Adversarial Attacks on Fairness of Graph Neural Networks
Binchi Zhang, Yushun Dong, Chen Chen +3
Fairness-aware graph neural networks (GNNs) have gained a surge of attention as they can reduce the bias of predictions on any demographic group (e.g., female) in graph-based appli…
GiGaMAE: Generalizable Graph Masked Autoencoder via Collaborative Latent Space Reconstruction
Yucheng Shi, Yushun Dong, Qiaoyu Tan +2
Self-supervised learning with masked autoencoders has recently gained popularity for its ability to produce effective image or textual representations, which can be applied to vari…
Spatial-Temporal Networks for Antibiogram Pattern Prediction
Xingbo Fu, Chen Chen, Yushun Dong +4
An antibiogram is a periodic summary of antibiotic resistance results of organisms from infected patients to selected antimicrobial drugs. Antibiograms help clinicians to understan…
Few-shot Node Classification with Extremely Weak Supervision
Song Wang, Yushun Dong, Kaize Ding +2
Few-shot node classification aims at classifying nodes with limited labeled nodes as references. Recent few-shot node classification methods typically learn from classes with abund…
RELIANT: Fair Knowledge Distillation for Graph Neural Networks
Yushun Dong, Binchi Zhang, Yiling Yuan +3
Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameter…
Interpreting Unfairness in Graph Neural Networks via Training Node Attribution
Yushun Dong, Song Wang, Jing Ma +2
Graph Neural Networks (GNNs) have emerged as the leading paradigm for solving graph analytical problems in various real-world applications. Nevertheless, GNNs could potentially ren…
Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications
Xingbo Fu, Binchi Zhang, Yushun Dong +2
Graph machine learning has gained great attention in both academia and industry recently. Most of the graph machine learning models, such as Graph Neural Networks (GNNs), are train…
On Structural Explanation of Bias in Graph Neural Networks
Yushun Dong, Song Wang, Yu Wang +2
Graph Neural Networks (GNNs) have shown satisfying performance in various graph analytical problems. Hence, they have become the \emph{de facto} solution in a variety of decision-m…
Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage
Yu Wang, Yuying Zhao, Yushun Dong +3
Graph Neural Networks (GNNs) have shown great power in learning node representations on graphs. However, they may inherit historical prejudices from training data, leading to discr…
FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs
Song Wang, Yushun Dong, Xiao Huang +2
Few-shot graph classification aims at predicting classes for graphs, given limited labeled graphs for each class. To tackle the bottleneck of label scarcity, recent works propose t…
Fairness in Graph Mining: A Survey
Yushun Dong, Jing Ma, Song Wang +2
Graph mining algorithms have been playing a significant role in myriad fields over the years. However, despite their promising performance on various graph analytical tasks, most o…