Haonan Ma

Biographic Data

ID9864268
NAMEHaonan Ma
GIVEN NAMESHaonan
FAMILY NAMEMa
SIGNATUREMA H
AFFILIATIONSZhejiang University of Technology
ORCID0009-0002-7451-9166
VERIFIEDYes
TOTAL WORKS5
TOTAL CITATIONS0
AUTHOR COUNT5
EDITOR COUNT0
FIRST PUBLICATION YEAR2022
LATEST PUBLICATION YEAR2026
H-INDEX0

  • Can Contrastive Learning Always be Trusted? Privacy Leakage Evaluation of Contrastive Learning for Graph Neural Networks

    Open AccessJinyin Chen, Wenbo Mu et al.ARTICLEIEEE Transactions on Computational…2026

    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

    Open AccessHaibin Zheng, Haiyang Xiong et al.ARTICLEIEEE Transactions on Computational…2024

    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

    Open AccessHaibin Zheng, Haiyang Xiong et al.ARTICLEIEEE Transactions on Computational…2024

    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

    Open AccessXuhong Zhang, Haibin Zheng et al.ARTICLEIEEE Transactions on Computational…2024

    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…

  • Hdrlm3d: A Deep Reinforcement Learning-Based Model with Human-like Perceptron and Policy for Crowd Evacuation in 3D Environments

    Open AccessDong Zhang, Wenhang Li et al.ARTICLEISPRS International Journal of…2022

    At present, a common drawback of crowd simulation models is that they are mainly simulated in (abstract) 2D environments, which limits the simulation of crowd behaviors observed in real 3D environments. Therefore, we propose a deep reinforcement learning-based model with human-like perceptron and policy for crowd evacuation in 3D environments (HDRLM3D). In HDRLM3D, we propose a vision-like ray perceptron (VLRP) and combine it with a redesigned gl…