Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Yanhong Xu 0003

dblp:180/4532-3 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
Machine learning › Graph learning › graph neural network
graph convolutional network
0.812024
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
0.812024
Sparse Multi-Relational Graph Convolutional Network for Multi-type Object Trajectory Prediction · IJCAI 2024
Robotics › Autonomous driving
trajectory prediction
0.812024
Sparse Multi-Relational Graph Convolutional Network for Multi-type Object Trajectory Prediction · IJCAI 2024
Network optimization and economics
resource allocation
0.812024
Efficient Throughput Maximization in Dynamic Rechargeable Networks · IEEE Trans. Mob. Comput. 2024
Network optimization and economics
throughput maximization
0.812024
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
YearPublicationVenuePosition
2024 Sparse Multi-Relational Graph Convolutional Network for Multi-type Object Trajectory Prediction
Liqi Yan, Yanhong Xu 0003, Zheng Wang 0007
IJCAI4
2024 Efficient Throughput Maximization in Dynamic Rechargeable Networks
abstract
In 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