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A Genetic Algorithm Using Diversity-Concern Principle to Solve Robust Influence Maximization Problem on Urban Transportation Networks
https://orcid.org/http://orcid.org/0000-0001-7363-7983
https://orcid.org/http://orcid.org/0000-0002-1796-7280
In recent years, the influence maximization has become a hot topic in the field of complex networks. The so-called influence maximization problem is to select a certain scale of nodes in a given network to work as seeds, and this subset can achieve the maximal information propagation effect. Networks like transportation systems are usually exposed to complex environment, its internal components are threatened by external disturbance, and its information transmission process is also at risk. The corresponding optimization problem can be modeled as the robust influence maximization problem (RIM). In cities, roads or intersections may face various failures or disruptions, such as natural disasters, traffic accidents or other emergencies. City managers need to design robust public transportation systems to minimize the impact of these situations on the overall transportation network. However, the existing work only considers the stable network structure or the damaged link, and lack the research on destructions caused by nodal failures. Also, the current optimization methods do not integrate the information of the search process well to solve the problem of robust influence maximization. To tackle these issues, the paper proposes a metric to evaluate seed robustness under node-specific attacks and a genetic algorithm called DC-GA-RIM. This method integrates the diversity concern principle and includes problem-oriented operators to improve its search ability. The effectiveness of DC-GA-RIM in solving RIM problems has been proven on a variety of networks. Stations and traffic hubs can be detected using the proposed method.
A Genetic Algorithm Using Diversity-Concern Principle to Solve Robust Influence Maximization Problem on Urban Transportation Networks
https://orcid.org/http://orcid.org/0000-0001-7363-7983
https://orcid.org/http://orcid.org/0000-0002-1796-7280
In recent years, the influence maximization has become a hot topic in the field of complex networks. The so-called influence maximization problem is to select a certain scale of nodes in a given network to work as seeds, and this subset can achieve the maximal information propagation effect. Networks like transportation systems are usually exposed to complex environment, its internal components are threatened by external disturbance, and its information transmission process is also at risk. The corresponding optimization problem can be modeled as the robust influence maximization problem (RIM). In cities, roads or intersections may face various failures or disruptions, such as natural disasters, traffic accidents or other emergencies. City managers need to design robust public transportation systems to minimize the impact of these situations on the overall transportation network. However, the existing work only considers the stable network structure or the damaged link, and lack the research on destructions caused by nodal failures. Also, the current optimization methods do not integrate the information of the search process well to solve the problem of robust influence maximization. To tackle these issues, the paper proposes a metric to evaluate seed robustness under node-specific attacks and a genetic algorithm called DC-GA-RIM. This method integrates the diversity concern principle and includes problem-oriented operators to improve its search ability. The effectiveness of DC-GA-RIM in solving RIM problems has been proven on a variety of networks. Stations and traffic hubs can be detected using the proposed method.
A Genetic Algorithm Using Diversity-Concern Principle to Solve Robust Influence Maximization Problem on Urban Transportation Networks
https://orcid.org/http://orcid.org/0000-0001-7363-7983
https://orcid.org/http://orcid.org/0000-0002-1796-7280
In recent years, the influence maximization has become a hot topic in the field of complex networks. The so-called influence maximization problem is to select a certain scale of nodes in a given network to work as seeds, and this subset can achieve the maximal information propagation effect. Networks like transportation systems are usually exposed to complex environment, its internal components are threatened by external disturbance, and its information transmission process is also at risk. The corresponding optimization problem can be modeled as the robust influence maximization problem (RIM). In cities, roads or intersections may face various failures or disruptions, such as natural disasters, traffic accidents or other emergencies. City managers need to design robust public transportation systems to minimize the impact of these situations on the overall transportation network. However, the existing work only considers the stable network structure or the damaged link, and lack the research on destructions caused by nodal failures. Also, the current optimization methods do not integrate the information of the search process well to solve the problem of robust influence maximization. To tackle these issues, the paper proposes a metric to evaluate seed robustness under node-specific attacks and a genetic algorithm called DC-GA-RIM. This method integrates the diversity concern principle and includes problem-oriented operators to improve its search ability. The effectiveness of DC-GA-RIM in solving RIM problems has been proven on a variety of networks. Stations and traffic hubs can be detected using the proposed method.
A Genetic Algorithm Using Diversity-Concern Principle to Solve Robust Influence Maximization Problem on Urban Transportation Networks
Lecture Notes in Civil Engineering
Guo, Wei (editor) / Qian, Kai (editor) / Tang, Honggang (editor) / Gong, Lei (editor) / Chen, Minghao (author) / Wang, Shuai (author)
International Conference on Green Building, Civil Engineering and Smart City ; 2023 ; Guiyang, China
2024-02-02
11 pages
Article/Chapter (Book)
Electronic Resource
English
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