Robustness and resilience of complex networks

O Artime, M Grassia, M De Domenico… - Nature Reviews …, 2024 - nature.com
Complex networks are ubiquitous: a cell, the human brain, a group of people and the
Internet are all examples of interconnected many-body systems characterized by …

Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial

V Nemani, L Biggio, X Huan, Z Hu, O Fink… - … Systems and Signal …, 2023 - Elsevier
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …

Spatial planning of urban communities via deep reinforcement learning

Y Zheng, Y Lin, L Zhao, T Wu, D **, Y Li - Nature Computational …, 2023 - nature.com
Effective spatial planning of urban communities plays a critical role in the sustainable
development of cities. Despite the convenience brought by geographic information systems …

A novel reinforcement learning-based hyper-heuristic for heterogeneous vehicle routing problem

W Qin, Z Zhuang, Z Huang, H Huang - Computers & Industrial Engineering, 2021 - Elsevier
This study investigates a practical heterogeneous vehicle routing problem that involves
routing a predefined fleet with different vehicle capacities to serve a series of customers to …

Structural Robustness of Complex Networks: A Survey of A Posteriori Measures [Feature]

Y Lou, L Wang, G Chen - IEEE Circuits and Systems Magazine, 2023 - ieeexplore.ieee.org
Network robustness is critical for various industrial and social networks against malicious
attacks, which has various meanings in different research contexts and here it refers to the …

Identifying influential nodes in complex networks via Transformer

L Chen, Y **, L Dong, M Zhao, C Li, X Liu… - Information Processing & …, 2024 - Elsevier
In the domain of complex networks, the identification of influential nodes plays a crucial role
in ensuring network stability and facilitating efficient information dissemination. Although the …

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 …

ToupleGDD: A fine-designed solution of influence maximization by deep reinforcement learning

T Chen, S Yan, J Guo, W Wu - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Aiming at selecting a small subset of nodes with maximum influence on networks, the
influence maximization (IM) problem has been extensively studied. Since it is# P-hard to …

Searching for spin glass ground states through deep reinforcement learning

C Fan, M Shen, Z Nussinov, Z Liu, Y Sun… - Nature …, 2023 - nature.com
Spin glasses are disordered magnets with random interactions that are, generally, in conflict
with each other. Finding the ground states of spin glasses is not only essential for …

Towards analyzing the robustness of the integrated global transportation network abstraction (IGTNA)

S Wandelt, X Sun, A Zhang - Transportation Research Part A: Policy and …, 2023 - Elsevier
The well-functioning of our transportation systems is essential for ensuring the mobility of
people and goods. According to the Sustainable Developmental Goals (SDG) adopted by …