56 citations · 206 across the 35 of their papers we have counts for
36 papers
Multi-view Correlation-aware Network Traffic Detection on Flow Hypergraph
Jiajun Zhou, Wentao Fu, Hao Song +2
As the Internet rapidly expands, the increasing complexity and diversity of network activities pose significant challenges to effective network governance and security regulation.…
Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
Xuanze Chen, Jiajun Zhou, Shanqing Yu +1
Graph neural networks excel at graph representation learning but struggle with heterophilous data and long-range dependencies. And graph transformers address these issues through s…
Lateral Movement Detection via Time-aware Subgraph Classification on Authentication Logs
Jiajun Zhou, Jiacheng Yao, Xuanze Chen +3
Lateral movement is a crucial component of advanced persistent threat (APT) attacks in networks. Attackers exploit security vulnerabilities in internal networks or IoT devices, exp…
Rethinking Graph Transformer Architecture Design for Node Classification
Jiajun Zhou, Xuanze Chen, Chenxuan Xie +3
Graph Transformer (GT), as a special type of Graph Neural Networks (GNNs), utilizes multi-head attention to facilitate high-order message passing. However, this also imposes severa…
Network Anomaly Traffic Detection via Multi-view Feature Fusion
Song Hao, Wentao Fu, Xuanze Chen +4
Traditional anomalous traffic detection methods are based on single-view analysis, which has obvious limitations in dealing with complex attacks and encrypted communications. In th…
Enhancing Ethereum Fraud Detection via Generative and Contrastive Self-supervision
Chenxiang Jin, Jiajun Zhou, Chenxuan Xie +3
The rampant fraudulent activities on Ethereum hinder the healthy development of the blockchain ecosystem, necessitating the reinforcement of regulations. However, multiple imbalanc…
Facilitating Feature and Topology Lightweighting: An Ethereum Transaction Graph Compression Method for Malicious Account Detection
Jiajun Zhou, Xuanze Chen, Shengbo Gong +4
Ethereum has become one of the primary global platforms for cryptocurrency, playing an important role in promoting the diversification of the financial ecosystem. However, the rela…
Exploring agent interaction patterns in the comment sections of fake and real news
Kailun Zhu, Songtao Peng, Jiaqi Nie +3
User comments on social media have been recognized as a crucial factor in distinguishing between fake and real news, with many studies focusing on the textual content of user react…
Dual-view Aware Smart Contract Vulnerability Detection for Ethereum
Jiacheng Yao, Maolin Wang, Wanqi Chen +4
The wide application of Ethereum technology has brought technological innovation to traditional industries. As one of Ethereum's core applications, smart contracts utilize diverse…
Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property Prediction
Zeyu Wang, Tianyi Jiang, Yao Lu +4
Recently, few-shot molecular property prediction (FSMPP) has garnered increasing attention. Despite impressive breakthroughs achieved by existing methods, they often overlook the i…
A Federated Parameter Aggregation Method for Node Classification Tasks with Different Graph Network Structures
Hao Song, Jiacheng Yao, Zhengxi Li +6
Over the past few years, federated learning has become widely used in various classical machine learning fields because of its collaborative ability to train data from multiple sou…
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…
PathMLP: Smooth Path Towards High-order Homophily
Jiajun Zhou, Chenxuan Xie, Shengbo Gong +4
Real-world graphs exhibit increasing heterophily, where nodes no longer tend to be connected to nodes with the same label, challenging the homophily assumption of classical graph n…
Subgraph Networks Based Contrastive Learning
Jinhuan Wang, Jiafei Shao, Zeyu Wang +3
Graph contrastive learning (GCL), as a self-supervised learning method, can solve the problem of annotated data scarcity. It mines explicit features in unannotated graphs to genera…
Clarify Confused Nodes via Separated Learning
Jiajun Zhou, Shengbo Gong, Xuanze Chen +4
Graph neural networks (GNNs) have achieved remarkable advances in graph-oriented tasks. However, real-world graphs invariably contain a certain proportion of heterophilous nodes, c…
Single Node Injection Label Specificity Attack on Graph Neural Networks via Reinforcement Learning
Dayuan Chen, Jian Zhang, Yuqian Lv +5
Graph neural networks (GNNs) have achieved remarkable success in various real-world applications. However, recent studies highlight the vulnerability of GNNs to malicious perturbat…
Discover Important Paths in the Knowledge Graph Based on Dynamic Relation Confidence
Shanqing Yu, Yijun Wu, Ran Gan +3
Most of the existing knowledge graphs are not usually complete and can be complemented by some reasoning algorithms. The reasoning method based on path features is widely used in t…
Dual-channel Early Warning Framework for Ethereum Ponzi Schemes
Jie Jin, Jiajun Zhou, Chengxiang Jin +3
Blockchain technology supports the generation and record of transactions, and maintains the fairness and openness of the cryptocurrency system. However, many fraudsters utilize sma…
SubGraph Networks based Entity Alignment for Cross-lingual Knowledge Graph
Shanqing Yu, Shihan Zhang, Jianlin Zhang +4
Entity alignment is the task of finding entities representing the same real-world object in two knowledge graphs(KGs). Cross-lingual knowledge graph entity alignment aims to discov…
Mixing Signals: Data Augmentation Approach for Deep Learning Based Modulation Recognition
Xinjie Xu, Zhuangzhi Chen, Dongwei Xu +5
With the rapid development of deep learning, automatic modulation recognition (AMR), as an important task in cognitive radio, has gradually transformed from traditional feature ext…
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…
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning
Jinyin Chen, Guohan Huang, Haibin Zheng +3
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 refl…
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…