Publications (52)
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…
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…
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…
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…
Adaptive Substructure-Aware Expert Model for Molecular Property Prediction
Tianyi Jiang, Zeyu Wang, Shanqing Yu +1
Molecular property prediction is essential for applications such as drug discovery and toxicity assessment. While Graph Neural Networks (GNNs) have shown promising results by model…
Efficient Parallel Genetic Algorithm for Perturbed Substructure Optimization in Complex Network
Shanqing Yu, Meng Zhou, Jintao Zhou +5
Evolutionary computing, particularly genetic algorithm (GA), is a combinatorial optimization method inspired by natural selection and the transmission of genetic information, which…
General2Specialized LLMs Translation for E-commerce
Kaidi Chen, Ben Chen, Dehong Gao +6
Existing Neural Machine Translation (NMT) models mainly handle translation in the general domain, while overlooking domains with special writing formulas, such as e-commerce and le…
LoRALib: A Standardized Benchmark for Evaluating LoRA-MoE Methods
Shaoheng Wang, Yao Lu, Yuqi Li +5
As a parameter efficient fine-tuning (PEFT) method, low-rank adaptation (LoRA) can save significant costs in storage and computing, but its strong adaptability to a single task is…
Instruction-Aligned Visual Attention for Mitigating Hallucinations in Large Vision-Language Models
Bin Li, Dehong Gao, Yeyuan Wang +4
Despite the significant success of Large Vision-Language models(LVLMs), these models still suffer hallucinations when describing images, generating answers that include non-existen…
M-Evolve: Structural-Mapping-Based Data Augmentation for Graph Classification
Jiajun Zhou, Jie Shen, Shanqing Yu +2
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…
CoF: Coarse to Fine-Grained Image Understanding for Multi-modal Large Language Models
Yeyuan Wang, Dehong Gao, Bin Li +7
The impressive performance of Large Language Model (LLM) has prompted researchers to develop Multi-modal LLM (MLLM), which has shown great potential for various multi-modal tasks.…
AgentS4D: Benchmarking Runtime Risks across the Execution Lifecycle of LLM-Based Workspace Agents
Jiajun Zhou, Zhaoxuan Ke, Jihang Ye +3
The paper presents AgentS4D, a sandboxed benchmark that evaluates runtime safety risks of large language model‑based workspace agents throughout their execution lifecycle, using a…
Traffic-MoE: A Sparse Foundation Model for Network Traffic Security Analysis
Jiajun Zhou, Changhui Sun, Wentao Fu +3
As adversaries increasingly weaponize encryption and protocol obfuscation to evade traffic detection, traditional methods are rendered obsolete, necessitating deep learning to unma…
Hierarchical Local-Global Feature Learning for Few-shot Malicious Traffic Detection
Songtao Peng, Lei Wang, Wu Shuai +4
With the rapid growth of internet traffic, malicious network attacks have become increasingly frequent and sophisticated, posing significant threats to global cybersecurity. Tradit…
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…
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…
GA Based Q-Attack on Community Detection
Jinyin Chen, Lihong Chen, Yixian Chen +4
Community detection plays an important role in social networks, since it can help to naturally divide the network into smaller parts so as to simplify network analysis. However, on…
ReCoG: Relational and Compact Context Graph Learning for Few-shot Molecular Property Prediction
Zeyu Wang, Xin Zheng, Yao Lu +3
Few-shot molecular property prediction (FSMPP) is essential in drug discovery and materials design, where high-quality labeled data are often scarce and expensive to obtain. Despit…
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…
Ponzi Scheme Detection in EthereumTransaction Network
Shanqing Yu, Jie Jin, Yunyi Xie +2
With the rapid growth of blockchain, an increasing number of users have been attracted and many implementations have been refreshed in different fields. Especially in the cryptocur…
Temporal-Amount Snapshot MultiGraph for Ethereum Transaction Tracking
Yunyi Xie, Jie Jin, Jian Zhang +2
With the wide application of blockchain in the financial field, the rise of various types of cybercrimes has brought great challenges to the security of blockchain. In order to bet…
CrossHGL: A Text-Free Foundation Model for Cross-Domain Heterogeneous Graph Learning
Xuanze Chen, Jiajun Zhou, Yadong Li +2
Heterogeneous graph representation learning (HGRL) is essential for modeling complex systems with diverse node and edge types. However, most existing methods are limited to closed-…
Few-shot Molecular Property Prediction: A Survey
Zeyu Wang, Tianyi Jiang, Huanchang Ma +6
AI-assisted molecular property prediction has become a promising technique in early-stage drug discovery and materials design in recent years. However, due to high-cost and complex…
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…
N2VSCDNNR: A Local Recommender System Based on Node2vec and Rich Information Network
Jinyin Chen, Yangyang Wu, Lu Fan +4
Recommender systems are becoming more and more important in our daily lives. However, traditional recommendation methods are challenged by data sparsity and efficiency, as the numb…
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…
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…
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…
Unveiling Latent Information in Transaction Hashes: Hypergraph Learning for Ethereum Ponzi Scheme Detection
Junhao Wu, Yixin Yang, Chengxiang Jin +5
With the widespread adoption of Ethereum, financial frauds such as Ponzi schemes have become increasingly rampant in the blockchain ecosystem, posing significant threats to the sec…
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…
MemSecBench: Tracking Agent Memory Poisoning from Persistence to Consequence and Repair
Xuanze Chen, Xukang Xie, Wentao Fu +3
The paper presents MemSecBench, a benchmark that evaluates how malicious instructions can persist, be executed, and be repaired in agent memory systems across different memory and…
The Structural Scalpel: Automated Contiguous Layer Pruning for Large Language Models
Yao Lu, Yuqi Li, Wenbin Xie +4
Although large language models (LLMs) have achieved revolutionary breakthroughs in many fields, their large model size and high computational cost pose significant challenges for p…
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…
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…
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…
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…
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…
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…
TSGN: Transaction Subgraph Networks for Identifying Ethereum Phishing Accounts
Jinhuan Wang, Pengtao Chen, Shanqing Yu +1
Blockchain technology and, in particular, blockchain-based transaction offers us information that has never been seen before in the financial world. In contrast to fiat currencies,…
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…
Target Defense Against Link-Prediction-Based Attacks via Evolutionary Perturbations
Shanqing Yu, Minghao Zhao, Chenbo Fu +4
In social networks, by removing some target-sensitive links, privacy protection might be achieved. However, some hidden links can still be re-observed by link prediction methods on…
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…
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…
Mixture of Message Passing Experts with Routing Entropy Regularization for Node Classification
Xuanze Chen, Jiajun Zhou, Yadong Li +3
Graph neural networks (GNNs) have achieved significant progress in graph-based learning tasks, yet their performance often deteriorates when facing heterophilous structures where c…
Learning How Much to Think: Difficulty-Aware Dynamic MoEs for Graph Node Classification
Jiajun Zhou, Yadong Li, Xuanze Chen +4
Mixture-of-Experts (MoE) architectures offer a scalable path for Graph Neural Networks (GNNs) in node classification tasks but typically rely on static and rigid routing strategies…
DSPC: Dual-Stage Progressive Compression Framework for Efficient Long-Context Reasoning
Yaxin Gao, Yao Lu, Zongfei Zhang +3
Large language models (LLMs) have achieved remarkable success in many natural language processing (NLP) tasks. To achieve more accurate output, the prompts used to drive LLMs have…
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…
Mapping Text to Multiplex Graph: Prompt Compression as Lévy Walk-Guided Graph Pruning
Yaxin Gao, Yao Lu, Jinhong Deng +7
Existing prompt compression methods treat text as flat token sequences, failing to capture the distributed nature of important information, which is often spread across multiple lo…
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…
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…
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…