Reverse percolation models for growing real-world networks
Reverse percolation analyzes the overall connectivity of a network after the addition of nodes or edges at predefined probabilities, which parallels the significance and application potential of traditional percolation theory. This paper explores the intersection between reverse percolation and netw...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Results in Physics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211379724007733 |
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| author | Tao Fu Caixia Zeng Liling Zou Chenguang Li |
| author_facet | Tao Fu Caixia Zeng Liling Zou Chenguang Li |
| author_sort | Tao Fu |
| collection | DOAJ |
| description | Reverse percolation analyzes the overall connectivity of a network after the addition of nodes or edges at predefined probabilities, which parallels the significance and application potential of traditional percolation theory. This paper explores the intersection between reverse percolation and network growth. It discusses the addition of a set of new nodes and edges simultaneously, offering insight into two distinct scenarios: random attachment, where all potential new edges have equal occupation probability, and preferential attachment, where the occupation probability of a potential new edge is proportional to the degree of the original network node it connects to. Reverse percolation analytic models are developed for these scenarios to compute the spanning cluster fraction and the percolation threshold. The accuracy of these models are evaluated across multiple real-world networks. Furthermore, the possibility of generating scale-free networks through single-step node and edge additions based on the preferential attachment mechanism is investigated. The results reveal that the double-unknown generating-function-based model proposed in this paper ensures universal applicability and accurate prediction across both investigated scenarios. In the context of preferential attachment, the simultaneous addition of nodes and edges may always produce a proportion of networks exhibiting scare-free structures. However, irrespective of the emergence of scale-free properties, the connectivity status of these networks can be effectively predicted using the proposed reverse percolation model. |
| format | Article |
| id | doaj-art-19a52c2c20e94e6d83673a82edca9fcc |
| institution | Kabale University |
| issn | 2211-3797 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Physics |
| spelling | doaj-art-19a52c2c20e94e6d83673a82edca9fcc2025-01-18T05:04:32ZengElsevierResults in Physics2211-37972025-01-0168108088Reverse percolation models for growing real-world networksTao Fu0Caixia Zeng1Liling Zou2Chenguang Li3Economics and Management School, Beijing University of Technology, Pingleyuan 100, Beijing 100124, ChinaEconomics and Management School, Beijing University of Technology, Pingleyuan 100, Beijing 100124, ChinaEconomics and Management School, Beijing University of Technology, Pingleyuan 100, Beijing 100124, ChinaEconomics and Management School, North China University of Technology, Jinyuanzhuanglu 5, Beijing 100144, China; Corresponding author.Reverse percolation analyzes the overall connectivity of a network after the addition of nodes or edges at predefined probabilities, which parallels the significance and application potential of traditional percolation theory. This paper explores the intersection between reverse percolation and network growth. It discusses the addition of a set of new nodes and edges simultaneously, offering insight into two distinct scenarios: random attachment, where all potential new edges have equal occupation probability, and preferential attachment, where the occupation probability of a potential new edge is proportional to the degree of the original network node it connects to. Reverse percolation analytic models are developed for these scenarios to compute the spanning cluster fraction and the percolation threshold. The accuracy of these models are evaluated across multiple real-world networks. Furthermore, the possibility of generating scale-free networks through single-step node and edge additions based on the preferential attachment mechanism is investigated. The results reveal that the double-unknown generating-function-based model proposed in this paper ensures universal applicability and accurate prediction across both investigated scenarios. In the context of preferential attachment, the simultaneous addition of nodes and edges may always produce a proportion of networks exhibiting scare-free structures. However, irrespective of the emergence of scale-free properties, the connectivity status of these networks can be effectively predicted using the proposed reverse percolation model.http://www.sciencedirect.com/science/article/pii/S2211379724007733Reverse percolationNetwork growthGenerating functionMessage passing processPhase transition |
| spellingShingle | Tao Fu Caixia Zeng Liling Zou Chenguang Li Reverse percolation models for growing real-world networks Results in Physics Reverse percolation Network growth Generating function Message passing process Phase transition |
| title | Reverse percolation models for growing real-world networks |
| title_full | Reverse percolation models for growing real-world networks |
| title_fullStr | Reverse percolation models for growing real-world networks |
| title_full_unstemmed | Reverse percolation models for growing real-world networks |
| title_short | Reverse percolation models for growing real-world networks |
| title_sort | reverse percolation models for growing real world networks |
| topic | Reverse percolation Network growth Generating function Message passing process Phase transition |
| url | http://www.sciencedirect.com/science/article/pii/S2211379724007733 |
| work_keys_str_mv | AT taofu reversepercolationmodelsforgrowingrealworldnetworks AT caixiazeng reversepercolationmodelsforgrowingrealworldnetworks AT lilingzou reversepercolationmodelsforgrowingrealworldnetworks AT chenguangli reversepercolationmodelsforgrowingrealworldnetworks |