activity
20242026
most citedA Survey on Open-Source Edge Computing Simulators and Emulators: The Computing and Networking Convergence Perspective

1 citations · 1 across the 5 of their papers we have counts for

collaborators

8 papers

cs.NI2026

Decision-Aware Semantic State Synchronization in Compute-First Networking

Jianpeng Qi, Chao Liu, Chengrui Wang +3

In Compute-First Networking (CFN), an Access Point (AP) makes task offloading decisions based on resource state information reported by a Service Node (SN). A fundamental challenge…

cs.NI20251 cited

A Survey on Open-Source Edge Computing Simulators and Emulators: The Computing and Networking Convergence Perspective

Jianpeng Qi, Chao Liu, Xiao Zhang +4

Edge computing, with its low latency, dynamic scalability, and location awareness, along with the convergence of computing and communication paradigms, has been successfully applie…

cs.NI2025

Efficient Information Updates in Compute-First Networking via Reinforcement Learning with Joint AoI and VoI

Jianpeng Qi, Chao Liu, Chengxiang Xu +3

Timely and efficient dissemination of service information is critical in compute-first networking systems, where user requests arrive dynamically and computing resources are constr…

cs.DB2025

Efficient Discovery of Motif Transition Process for Large-Scale Temporal Graphs

Zhiyuan Zheng, Jianpeng Qi, Jiantao Li +3

Understanding the dynamic transition of motifs in temporal graphs is essential for revealing how graph structures evolve over time, identifying critical patterns, and predicting fu…

cs.SI2025

Correlation-Attention Masked Temporal Transformer for User Identity Linkage Using Heterogeneous Mobility Data

Ziang Yan, Xingyu Zhao, Hanqing Ma +4

With the rise of social media and Location-Based Social Networks (LBSN), check-in data across platforms has become crucial for User Identity Linkage (UIL). These data not only reve…

cs.LG2025

Scalable Trajectory-User Linking with Dual-Stream Representation Networks

Hao Zhang, Wei Chen, Xingyu Zhao +3

Trajectory-user linking (TUL) aims to match anonymous trajectories to the most likely users who generated them, offering benefits for a wide range of real-world spatio-temporal app…