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 …
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 …
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 …
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 …
Cblab: Supporting the training of large-scale traffic control policies with scalable traffic simulation
Traffic simulation provides interactive data for the optimization of traffic control policies.
However, existing traffic simulators are limited by their lack of scalability and shortage in …
However, existing traffic simulators are limited by their lack of scalability and shortage in …
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 …