activity
20202026
most citedDropMessage: Unifying Random Dropping for Graph Neural Networks

64 citations · 126 across the 31 of their papers we have counts for

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Showing 2026Show all

6 papers · 1 filter

cs.CR2026

Guarding Organizations Against Malware Risk: A Novel Graph-Based Malware Detection Method

Yinan Gao, Jiarong Xu, Xiaohang Zhao +1

Organizational digitalization expands cybersecurity risks, making cybersecurity an increasingly important research area in Information Systems (IS). Among these risks, malware has…

cs.CR2026

Traceable LLM Reasoning for Fake-Order Fraud Detection

Siqi You, Bingsong Xu, Zhixian Zheng +4

Detecting fake-order fraud at scale remains a critical challenge for large online-to-offline (O2O) service platforms, as existing approaches often rely on expert-designed features,…

cs.IR2026

ChartWalker: Benchmarking the Cross-Chart RAG Task with Hierarchical Knowledge Graphs

Ning Tang, Chenghan Xie, Hanyang Yuan +6

Cross-Chart Retrieval-Augmented Generation (RAG) is critical for complex multi-modal analytical tasks in scientific, business, and political domains. However, existing benchmarks e…

cs.LG2026

Privacy-Preserving Credit Risk Prediction with Alternative Data

Hongzhe Zhang, Jiarong Xu, Jing He +1

Credit risk prediction is a critical problem in the consumer credit industry. Traditionally, financial institutions construct credit risk prediction models using borrowers' demogra…

cs.SI2026

PolicySim: An LLM-Based Agent Social Simulation Sandbox for Proactive Policy Optimization

Renhong Huang, Ning Tang, Jiarong Xu +6

Social platforms serve as central hubs for information exchange, where user behaviors and platform interventions jointly shape opinions. However, intervention policies like recomme…

cs.AI2026

RAPO: Expanding Exploration for LLM Agents via Retrieval-Augmented Policy Optimization

Siwei Zhang, Yun Xiong, Xi Chen +4

Agentic Reinforcement Learning (Agentic RL) has shown remarkable potential in large language model-based (LLM) agents. These works can empower LLM agents to tackle complex tasks vi…