Haibin Zheng
Biographic Data
| ID | 10054064 |
|---|---|
| NAME | Haibin Zheng |
| GIVEN NAMES | Haibin |
| FAMILY NAME | Zheng |
| SIGNATURE | ZHENG H |
| AFFILIATIONS | Zhejiang University of Technology |
| ORCID | 0000-0002-8997-5343 |
| VERIFIED | Yes |
| TOTAL WORKS | 8 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 8 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Can Contrastive Learning Always be Trusted? Privacy Leakage Evaluation of Contrastive Learning for Graph Neural Networks
Contrastive learning, as an efficient unsupervised learning, has been extensively studied for improving graph neural network (GNN) in graph-structured data mining. With the wider application of GNN, recent work has revealed that it is vulnerable toward privacy leakage (e.g., property inference). How dose contrastive learning influence privacy leakage of GNN? To address the issue, for the first time we comprehensively evaluate the privacy leakage …
Link-Backdoor: Backdoor Attack on Link Prediction via Node Injection
Link prediction, inferring the undiscovered or potential links of the graph, is widely applied in the real world. By facilitating labeled links of the graph as the training data, numerous deep learning-based link prediction methods have been studied, which have dominant prediction accuracy compared with nondeep methods. However, the threats of maliciously crafted training graphs will leave a specific backdoor in the deep model; thus, when some sp…
Motif-Backdoor: Rethinking the Backdoor Attack on Graph Neural Networks via Motifs
Graph neural network (GNN) with a powerful representation capability has been widely applied to various areas. Recent works have exposed that GNN is vulnerable to the backdoor attack, i.e., models trained with maliciously crafted training samples are easily fooled by patched samples. Most of the proposed studies launch the backdoor attack using a trigger that is either the randomly generated subgraph [e.g., erdős-rényi backdoor (ER-B)] for less c…
Tegdetector: A Phishing Detector That Knows Evolving Transaction Behaviors
Recently, phishing scams have posed a significant threat to blockchains. Phishing detectors direct their efforts in hunting phishing addresses. Most of the detectors extract target addresses’ transaction behavior features by random walking or constructing static subgraphs. The random walking methods, unfortunately, usually miss structural information due to limited sampling sequence length, while the static subgraph methods tend to ignore tempora…
Graphfool: Targeted Label Adversarial Attack on Graph Embedding
Graph embedding learns low-dimensional representations for nodes or edges on the graph, which is widely applied in many real-world applications. Excessive graph mining promotes the research of attack methods on graph embedding. Most attack methods generate perturbations that maximize the deviation of the prediction confidence. They are difficult to accurately misclassify the instances into the target label, and the nonminimized perturbations are …
Graph-Fraudster: Adversarial Attacks on Graph Neural Network-Based Vertical Federated Learning
Graph neural network (GNN) has achieved great success on graph representation learning. Challenged by large-scale private data collected from user side, GNN may not be able to reflect the excellent performance, without rich features and complete adjacent relationships. Addressing the problem, vertical federated learning (VFL) is proposed to implement local data protection through training a global model collaboratively. Consequently, for graph-st…
Mga: Momentum Gradient Attack on Network
The adversarial attack methods based on gradient information can adequately find the perturbations, that is, the combinations of rewired links, thereby reducing the effectiveness of the deep learning model-based graph embedding algorithms, but it is also easy to fall into a local optimum. Therefore, this article proposes a momentum gradient attack (MGA) against the graph convolutional network (GCN) model, which can achieve more aggressive attacks…
Smoothing Adversarial Training for GNN
Recently, a graph neural network (GNN) was proposed to analyze various graphs/networks, which has been proven to outperform many other network analysis methods. However, it is also shown that such state-of-the-art methods suffer from adversarial attacks, i.e., carefully crafted adversarial networks with slight perturbation on clean one may invalid these methods on lots of applications, such as network embedding, node classification, link predicti…
No prominent works on this page.
Mga: Momentum Gradient Attack on Network
The adversarial attack methods based on gradient information can adequately find the perturbations, that is, the combinations of rewired links, thereby reducing the effectiveness of the deep learning model-based graph embedding algorithms, but it is also easy to fall into a local optimum. Therefore, this article proposes a momentum gradient attack (MGA) against the graph convolutional network (GCN) model, which can achieve more aggressive attacks…
Smoothing Adversarial Training for GNN
Recently, a graph neural network (GNN) was proposed to analyze various graphs/networks, which has been proven to outperform many other network analysis methods. However, it is also shown that such state-of-the-art methods suffer from adversarial attacks, i.e., carefully crafted adversarial networks with slight perturbation on clean one may invalid these methods on lots of applications, such as network embedding, node classification, link predicti…
Graphfool: Targeted Label Adversarial Attack on Graph Embedding
Graph embedding learns low-dimensional representations for nodes or edges on the graph, which is widely applied in many real-world applications. Excessive graph mining promotes the research of attack methods on graph embedding. Most attack methods generate perturbations that maximize the deviation of the prediction confidence. They are difficult to accurately misclassify the instances into the target label, and the nonminimized perturbations are …
Graph-Fraudster: Adversarial Attacks on Graph Neural Network-Based Vertical Federated Learning
Graph neural network (GNN) has achieved great success on graph representation learning. Challenged by large-scale private data collected from user side, GNN may not be able to reflect the excellent performance, without rich features and complete adjacent relationships. Addressing the problem, vertical federated learning (VFL) is proposed to implement local data protection through training a global model collaboratively. Consequently, for graph-st…
Link-Backdoor: Backdoor Attack on Link Prediction via Node Injection
Link prediction, inferring the undiscovered or potential links of the graph, is widely applied in the real world. By facilitating labeled links of the graph as the training data, numerous deep learning-based link prediction methods have been studied, which have dominant prediction accuracy compared with nondeep methods. However, the threats of maliciously crafted training graphs will leave a specific backdoor in the deep model; thus, when some sp…
Motif-Backdoor: Rethinking the Backdoor Attack on Graph Neural Networks via Motifs
Graph neural network (GNN) with a powerful representation capability has been widely applied to various areas. Recent works have exposed that GNN is vulnerable to the backdoor attack, i.e., models trained with maliciously crafted training samples are easily fooled by patched samples. Most of the proposed studies launch the backdoor attack using a trigger that is either the randomly generated subgraph [e.g., erdős-rényi backdoor (ER-B)] for less c…
Tegdetector: A Phishing Detector That Knows Evolving Transaction Behaviors
Recently, phishing scams have posed a significant threat to blockchains. Phishing detectors direct their efforts in hunting phishing addresses. Most of the detectors extract target addresses’ transaction behavior features by random walking or constructing static subgraphs. The random walking methods, unfortunately, usually miss structural information due to limited sampling sequence length, while the static subgraph methods tend to ignore tempora…
Can Contrastive Learning Always be Trusted? Privacy Leakage Evaluation of Contrastive Learning for Graph Neural Networks
Contrastive learning, as an efficient unsupervised learning, has been extensively studied for improving graph neural network (GNN) in graph-structured data mining. With the wider application of GNN, recent work has revealed that it is vulnerable toward privacy leakage (e.g., property inference). How dose contrastive learning influence privacy leakage of GNN? To address the issue, for the first time we comprehensively evaluate the privacy leakage …
Computer Science (7 works) · Graph (6 works) · Advanced Graph Neural Networks (5 works) · Artificial Intelligence (5 works) · Theoretical Computer Science (5 works) · Computer security (4 works) · Adversarial Robustness in Machine Learning (3 works) · Adversarial system (3 works) · Network Security and Intrusion Detection (3 works) · Algorithm (2 works)