How to build a graph-based deep learning architecture in traffic domain: A survey
In recent years, various deep learning architectures have been proposed to solve complex
challenges (eg spatial dependency, temporal dependency) in traffic domain, which have …
challenges (eg spatial dependency, temporal dependency) in traffic domain, which have …
Advances in spatiotemporal graph neural network prediction research
Y Wang - International Journal of Digital Earth, 2023 - Taylor & Francis
Being a kind of non-Euclidean data, spatiotemporal graph data exists everywhere from traffic
flow, air quality index to crime case, etc. Unlike the raster data, the irregular and disordered …
flow, air quality index to crime case, etc. Unlike the raster data, the irregular and disordered …
Causal incremental graph convolution for recommender system retraining
The real-world recommender system needs to be regularly retrained to keep with the new
data. In this work, we consider how to efficiently retrain graph convolution network (GCN) …
data. In this work, we consider how to efficiently retrain graph convolution network (GCN) …
A survey on influence maximization: From an ml-based combinatorial optimization
Influence Maximization (IM) is a classical combinatorial optimization problem, which can be
widely used in mobile networks, social computing, and recommendation systems. It aims at …
widely used in mobile networks, social computing, and recommendation systems. It aims at …
Contingency-aware influence maximization: A reinforcement learning approach
The influence maximization (IM) problem aims at finding a subset of seed nodes in a social
network that maximize the spread of influence. In this study, we focus on a sub-class of IM …
network that maximize the spread of influence. In this study, we focus on a sub-class of IM …
Mdpfuzz: testing models solving markov decision processes
The Markov decision process (MDP) provides a mathematical frame-work for modeling
sequential decision-making problems, many of which are crucial to security and safety, such …
sequential decision-making problems, many of which are crucial to security and safety, such …
Reinforcement learning on graphs: A survey
M Nie, D Chen, D Wang - IEEE Transactions on Emerging …, 2023 - ieeexplore.ieee.org
Graph mining tasks arise from many different application domains, including social
networks, biological networks, transportation, and E-commerce, which have been receiving …
networks, biological networks, transportation, and E-commerce, which have been receiving …
CTRL: Cooperative Traffic Tolling via Reinforcement Learning
Y Wang, H Jin, G Zheng - Proceedings of the 31st ACM International …, 2022 - dl.acm.org
People have been working long to tackle the traffic congestion problem. Among the different
measures, traffic tolling has been recognized as an effective way to mitigate citywide …
measures, traffic tolling has been recognized as an effective way to mitigate citywide …
Depthwise convolution for multi-agent communication with enhanced mean-field approximation
Multi-Agent settings remain a fundamental challenge in the reinforcement learning (RL)
domain due to the partial observability and the lack of accurate real-time interactions across …
domain due to the partial observability and the lack of accurate real-time interactions across …
A dynamic and deadline-oriented road pricing mechanism for urban traffic management
J Jin, X Zhu, B Wu, J Zhang… - Tsinghua Science and …, 2021 - ieeexplore.ieee.org
Road pricing is an urban traffic management mechanism to reduce traffic congestion.
Currently, most of the road pricing systems based on predefined charging tolls fail to …
Currently, most of the road pricing systems based on predefined charging tolls fail to …