132 citations · 683 across the 107 of their papers we have counts for
25 papers · 1 filter
Double Whammy: Stealthy Data Manipulation aided Reconstruction Attack on Graph Federated Learning
Jinyin Chen, Minying Ma, Haibin Zheng +1
Recent research has constructed successful graph reconstruction attack (GRA) on GFL. But these attacks are still challenged in aspects of effectiveness and stealth. To address the…
Improving Network Degree Correlation by Degree-preserving Rewiring
Shuo Zou, Bo Zhou, Qi Xuan
Degree correlation is a crucial measure in networks, significantly impacting network topology and dynamical behavior. The degree sequence of a network is a significant characterist…
Attacking The Assortativity Coefficient Under A Rewiring Strategy
Shuo Zou, Bo Zhou, Qi Xuan
Degree correlation is an important characteristic of networks, which is usually quantified by the assortativity coefficient. However, concerns arise about changing the assortativit…
Multi-triplet Feature Augmentation for Ponzi Scheme Detection in Ethereum
Chengxiang Jin, Jiajun Zhou, Shengbo Gong +2
Blockchain technology revolutionizes the Internet, but also poses increasing risks, particularly in cryptocurrency finance. On the Ethereum platform, Ponzi schemes, phishing scams,…
MONA: An Efficient and Scalable Strategy for Targeted k-Nodes Collapse
Yuqian Lv, Bo Zhou, Jinhuan Wang +2
The concept of k-core plays an important role in measuring the cohesiveness and engagement of a network. And recent studies have shown the vulnerability of k-core under adversarial…
Targeted k-node Collapse Problem: Towards Understanding the Robustness of Local k-core Structure
Yuqian Lv, Bo Zhou, Jinhuan Wang +1
The concept of k-core, which indicates the largest induced subgraph where each node has k or more neighbors, plays a significant role in measuring the cohesiveness and the engageme…
Phishing Fraud Detection on Ethereum using Graph Neural Network
Panpan Li, Yunyi Xie, Xinyao Xu +2
Blockchain has widespread applications in the financial field but has also attracted increasing cybercrimes. Recently, phishing fraud has emerged as a major threat to blockchain se…
Behavior-aware Account De-anonymization on Ethereum Interaction Graph
Jiajun Zhou, Chenkai Hu, Jianlei Chi +3
Blockchain technology has the characteristics of decentralization, traceability and tamper-proof, which creates a reliable decentralized trust mechanism, further accelerating the d…
Inductive Subgraph Embedding for Link Prediction
Chunyu Miao, Chenxuan Xie, Jiajun Zhou +3
Graph representation learning (GRL) has emerged as a powerful technique for solving graph analytics tasks. It can effectively convert discrete graph data into a low-dimensional spa…
Null Model-Based Data Augmentation for Graph Classification
Qi Xuan, Zeyu Wang, Jinhuan Wang +3
In network science, the null model is typically used to generate a series of graphs based on randomization as a term of comparison to verify whether a network in question displays…
COREATTACK: Breaking Up the Core Structure of Graphs
Bo Zhou, Yuqian Lv, Jinhuan Wang +2
The concept of k-core in complex networks plays a key role in many applications, e.g., understanding the global structure, or identifying central/critical nodes, of a network. A ma…
The Robustness of Graph k-shell Structure under Adversarial Attacks
B. Zhou, Y. Q. Lv, Y. C. Mao +3
The k-shell decomposition plays an important role in unveiling the structural properties of a network, i.e., it is widely adopted to find the densest part of a network across a bro…
DeepInsight: Interpretability Assisting Detection of Adversarial Samples on Graphs
Junhao Zhu, Yalu Shan, Jinhuan Wang +3
With the rapid development of artificial intelligence, a number of machine learning algorithms, such as graph neural networks have been proposed to facilitate network analysis or g…
Identity Inference on Blockchain using Graph Neural Network
Jie Shen, Jiajun Zhou, Yunyi Xie +2
The anonymity of blockchain has accelerated the growth of illegal activities and criminal behaviors on cryptocurrency platforms. Although decentralization is one of the typical cha…
Sampling Subgraph Network with Application to Graph Classification
Jinhuan Wang, Pengtao Chen, Bin Ma +4
Graphs are naturally used to describe the structures of various real-world systems in biology, society, computer science etc., where subgraphs or motifs as basic blocks play an imp…
Data Augmentation for Graph Classification
Jiajun Zhou, Jie Shen, Qi Xuan
Graph classification, which aims to identify the category labels of graphs, plays a significant role in drug classification, toxicity detection, protein analysis etc. However, the…
MGA: Momentum Gradient Attack on Network
Jinyin Chen, Yixian Chen, Haibin Zheng +4
The adversarial attack methods based on gradient information can adequately find the perturbations, that is, the combinations of rewired links, thereby reducing the effectiveness o…
Time-aware Gradient Attack on Dynamic Network Link Prediction
Jinyin Chen, Jian Zhang, Zhi Chen +2
In network link prediction, it is possible to hide a target link from being predicted with a small perturbation on network structure. This observation may be exploited in many real…
RobustECD: Enhancement of Network Structure for Robust Community Detection
Jiajun Zhou, Zhi Chen, Min Du +4
Community detection, which focuses on clustering vertex interactions, plays a significant role in network analysis. However, it also faces numerous challenges like missing data and…
Multiscale Evolutionary Perturbation Attack on Community Detection
Jinyin Chen, Yixian Chen, Lihong Chen +2
Community detection, aiming to group nodes based on their connections, plays an important role in network analysis, since communities, treated as meta-nodes, allow us to create a l…
Unsupervised Euclidean Distance Attack on Network Embedding
Shanqing Yu, Jun Zheng, Jinhuan Wang +6
Considering the wide application of network embedding methods in graph data mining, inspired by the adversarial attack in deep learning, this paper proposes a Genetic Algorithm (GA…
Can Adversarial Network Attack be Defended?
Jinyin Chen, Yangyang Wu, Xiang Lin +1
Machine learning has been successfully applied to complex network analysis in various areas, and graph neural networks (GNNs) based methods outperform others. Recently, adversarial…
E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction
Jinyin Chen, Jian Zhang, Xuanheng Xu +4
Predicting the potential relations between nodes in networks, known as link prediction, has long been a challenge in network science. However, most studies just focused on link pre…
A Self-Learning Information Diffusion Model for Smart Social Networks
Qi Xuan, Xincheng Shu, Zhongyuan Ruan +3
In this big data era, more and more social activities are digitized thereby becoming traceable, and thus the studies of social networks attract increasing attention from academia.…