72 citations · 405 across the 33 of their papers we have counts for
13 papers · 1 filter
Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks
Binghui Wang, Jinyuan Jia, Neil Zhenqiang Gong
Semi-supervised node classification on graph-structured data has many applications such as fraud detection, fake account and review detection, user's private attribute inference in…
Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective
Jingwei Sun, Ang Li, Binghui Wang +3
Federated learning (FL) is a popular distributed learning framework that can reduce privacy risks by not explicitly sharing private data. However, recent works demonstrated that sh…
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs
Binghui Wang, Ang Li, Hai Li +1
Graph-based semi-supervised node classification (GraphSSC) has wide applications, ranging from networking and security to data mining and machine learning, etc. However, existing c…
Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations
Jinyuan Jia, Binghui Wang, Xiaoyu Cao +2
Top-k predictions are used in many real-world applications such as machine learning as a service, recommender systems, and web searches. -norm adversarial perturbation char…
Robust and Verifiable Information Embedding Attacks to Deep Neural Networks via Error-Correcting Codes
Jinyuan Jia, Binghui Wang, Neil Zhenqiang Gong
In the era of deep learning, a user often leverages a third-party machine learning tool to train a deep neural network (DNN) classifier and then deploys the classifier as an end-us…
Reinforcement Learning-based Black-Box Evasion Attacks to Link Prediction in Dynamic Graphs
Houxiang Fan, Binghui Wang, Pan Zhou +6
Link prediction in dynamic graphs (LPDG) is an important research problem that has diverse applications such as online recommendations, studies on disease contagion, organizational…
Efficient, Direct, and Restricted Black-Box Graph Evasion Attacks to Any-Layer Graph Neural Networks via Influence Function
Binghui Wang, Tianxiang Zhou, Minhua Lin +5
Graph neural network (GNN), the mainstream method to learn on graph data, is vulnerable to graph evasion attacks, where an attacker slightly perturbing the graph structure can fool…
LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets
Ang Li, Jingwei Sun, Binghui Wang +4
Federated learning is a popular distributed machine learning paradigm with enhanced privacy. Its primary goal is learning a global model that offers good performance for the partic…
Certified Robustness of Graph Neural Networks against Adversarial Structural Perturbation
Binghui Wang, Jinyuan Jia, Xiaoyu Cao +1
Graph neural networks (GNNs) have recently gained much attention for node and graph classification tasks on graph-structured data. However, multiple recent works showed that an att…
Backdoor Attacks to Graph Neural Networks
Zaixi Zhang, Jinyuan Jia, Binghui Wang +1
In this work, we propose the first backdoor attack to graph neural networks (GNN). Specifically, we propose a \emph{subgraph based backdoor attack} to GNN for graph classification.…
Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability
Nathan Inkawhich, Kevin J Liang, Binghui Wang +3
We consider the blackbox transfer-based targeted adversarial attack threat model in the realm of deep neural network (DNN) image classifiers. Rather than focusing on crossing decis…
On Certifying Robustness against Backdoor Attacks via Randomized Smoothing
Binghui Wang, Xiaoyu Cao, Jinyuan jia +1
Backdoor attack is a severe security threat to deep neural networks (DNNs). We envision that, like adversarial examples, there will be a cat-and-mouse game for backdoor attacks, i.…
Certified Robustness of Community Detection against Adversarial Structural Perturbation via Randomized Smoothing
Jinyuan Jia, Binghui Wang, Xiaoyu Cao +1
Community detection plays a key role in understanding graph structure. However, several recent studies showed that community detection is vulnerable to adversarial structural pertu…