activity
20242026
most citedFacilitating Feature and Topology Lightweighting: An Ethereum Transaction Graph Compression Method for Malicious Account Detection

1 citations · 2 across the 9 of their papers we have counts for

collaborators

10 papers

cs.SE2026

AgentS4D: Benchmarking Runtime Risks across the Execution Lifecycle of LLM-Based Workspace Agents

Jiajun Zhou, Zhaoxuan Ke, Jihang Ye +3

Large language model (LLM)-based workspace agents execute stateful, multi-step workflows across heterogeneous resources, external tools, and persistent state. Their safety must the…

cs.CR2026

MemSecBench: Tracking Agent Memory Poisoning from Persistence to Consequence and Repair

Xuanze Chen, Xukang Xie, Wentao Fu +3

Memory systems allow agents to retain and reuse information from past interactions, but they can also let malicious content persist. A malicious instruction crafted by an attacker…

cs.LG2026

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…

cs.LG2026

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-…

cs.LG2025

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…

cs.LG2024★ 1 cited

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…

cs.CR2024

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…

cs.LG2024

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…

cs.LG2024

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…

cs.CR2024★ 1 cited

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…