Demo Data from EDBT 2026 / reviewers in the wild / expert
Yanhong Xu 0003
dblp:180/4532-3
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2024
0000-0003-1909-576XORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Computer networks · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Graph learning · 67% Autonomous driving · 33% | |
| Computer networks
1 paper |
Network optimization and economics · 87% Wireless networking · 13% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Graph learning › graph neural network
graph convolutional network |
0.8 | 1 | 2024 | Sparse Multi-Relational Graph Convolutional Network for Multi-type Object Trajectory Prediction · IJCAI 2024 |
Machine learning › Graph learning › graph neural network › heterogeneous graph neural network
multi-relational graph neural network |
0.8 | 1 | 2024 | Sparse Multi-Relational Graph Convolutional Network for Multi-type Object Trajectory Prediction · IJCAI 2024 |
Robotics › Autonomous driving
trajectory prediction |
0.8 | 1 | 2024 | Sparse Multi-Relational Graph Convolutional Network for Multi-type Object Trajectory Prediction · IJCAI 2024 |
Network optimization and economics
resource allocation |
0.8 | 1 | 2024 | Efficient Throughput Maximization in Dynamic Rechargeable Networks · IEEE Trans. Mob. Comput. 2024 |
Network optimization and economics
throughput maximization |
0.8 | 1 | 2024 | Efficient Throughput Maximization in Dynamic Rechargeable Networks · IEEE Trans. Mob. Comput. 2024 |
Methods — techniques the papers use, named apart from their topics
time-expanded graph · 0.8sparse multi-relational learning · 0.8markov approximation · 0.8lyapunov optimization · 0.8graph convolutional network · 0.8garg-könemann framework · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Sparse Multi-Relational Graph Convolutional Network for Multi-type Object Trajectory Prediction
Liqi Yan, Yanhong Xu 0003, Zheng Wang 0007 |
IJCAI | 4 |
| 2024 | Efficient Throughput Maximization in Dynamic Rechargeable NetworksabstractIn the Dynamic Rechargeable Networks (DRNs), to maximize the throughput by efficient energy allocation, the existing studies usually consider the spatio-temporal dynamic factors of the harvested energy, and seldom the network dynamic factors simultaneously, such as the time variable network resources and wireless interference. To take the network dynamic factors together, this paper studies the quite challenging problem, the network throughput maximization in the DRNs. We introduce the Time-Expanded Graph (TEG) to describe the above dynamic factors in an obvious way and design the Single Pair Throughput maximization (SPT) algorithm based on TEG. In the case of multiple pairs of source-targets, this paper introduces the Garg and Könemann's framework and then designs the Multiple Pairs Throughput (MPT) algorithm to maximize the overall throughput of all pairs. To reduce the time complexity, this paper proposes a Distributed and Parallel Throughput algorithm (DPT). In real applications, the network dynamic factors may not be known in advance. This paper proposes an Online Time-Span Algorithm (OTA) with Markov approximation and Lyapunov optimization, and conducts the extensive numerical evaluation based on the simulated data and the data collected by our real system. The numerical simulation results demonstrate the throughput improvement of our algorithms. Yanhong Xu 0003, Jiacheng Wang 0004, Hanxiang Wang, Feng Xia 0001 |
IEEE Trans. Mob. Comput. | 2 |