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Robustness and resilience of complex networks
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 …
Internet are all examples of interconnected many-body systems characterized by …
Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial
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 …
essential layer of safety assurance that could lead to more principled decision making by …
Spatial planning of urban communities via deep reinforcement learning
Effective spatial planning of urban communities plays a critical role in the sustainable
development of cities. Despite the convenience brought by geographic information systems …
development of cities. Despite the convenience brought by geographic information systems …
A novel reinforcement learning-based hyper-heuristic for heterogeneous vehicle routing problem
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 …
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]
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 …
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 …
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 …
networks, biological networks, transportation, and E-commerce, which have been receiving …
ToupleGDD: A fine-designed solution of influence maximization by deep reinforcement learning
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 …
influence maximization (IM) problem has been extensively studied. Since it is# P-hard to …
Searching for spin glass ground states through deep reinforcement learning
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 …
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)
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 …
people and goods. According to the Sustainable Developmental Goals (SDG) adopted by …