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Two-way selection on complex weighted networks. Neural Comput Appl 2019. [DOI: 10.1007/s00521-017-3122-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Assortative Mating: Encounter-Network Topology and the Evolution of Attractiveness. Sci Rep 2017; 7:45107. [PMID: 28345625 PMCID: PMC5366857 DOI: 10.1038/srep45107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Accepted: 02/17/2017] [Indexed: 11/09/2022] Open
Abstract
We model a social-encounter network where linked nodes match for reproduction in a manner depending probabilistically on each node's attractiveness. The developed model reveals that increasing either the network's mean degree or the "choosiness" exercised during pair formation increases the strength of positive assortative mating. That is, we note that attractiveness is correlated among mated nodes. Their total number also increases with mean degree and selectivity during pair formation. By iterating over the model's mapping of parents onto offspring across generations, we study the evolution of attractiveness. Selection mediated by exclusion from reproduction increases mean attractiveness, but is rapidly balanced by skew in the offspring distribution of highly attractive mated pairs.
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Bai YN, Huang N, Wang L, Wu ZX. Robustness and Vulnerability of Networks with Dynamical Dependency Groups. Sci Rep 2016; 6:37749. [PMID: 27892940 PMCID: PMC5125273 DOI: 10.1038/srep37749] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Accepted: 11/02/2016] [Indexed: 11/08/2022] Open
Abstract
The dependency property and self-recovery of failure nodes both have great effects on the robustness of networks during the cascading process. Existing investigations focused mainly on the failure mechanism of static dependency groups without considering the time-dependency of interdependent nodes and the recovery mechanism in reality. In this study, we present an evolving network model consisting of failure mechanisms and a recovery mechanism to explore network robustness, where the dependency relations among nodes vary over time. Based on generating function techniques, we provide an analytical framework for random networks with arbitrary degree distribution. In particular, we theoretically find that an abrupt percolation transition exists corresponding to the dynamical dependency groups for a wide range of topologies after initial random removal. Moreover, when the abrupt transition point is above the failure threshold of dependency groups, the evolving network with the larger dependency groups is more vulnerable; when below it, the larger dependency groups make the network more robust. Numerical simulations employing the Erdős-Rényi network and Barabási-Albert scale free network are performed to validate our theoretical results.
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Affiliation(s)
- Ya-Nan Bai
- School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, P.R. China
| | - Ning Huang
- School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, P.R. China
- Key Laboratory of Science & Technology on Reliability & Environmental Engineering, Beihang University, Beijing, 100191, P.R. China
| | - Lei Wang
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, P.R. China
| | - Zhi-Xi Wu
- Institute of Computational Physics and Complex Systems, Lanzhou University, Lanzhou, Gansu, 730000, P.R. China
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Jia T, Spivey RF, Szymanski B, Korniss G. An Analysis of the Matching Hypothesis in Networks. PLoS One 2015; 10:e0129804. [PMID: 26083728 PMCID: PMC4470921 DOI: 10.1371/journal.pone.0129804] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2015] [Accepted: 05/13/2015] [Indexed: 11/19/2022] Open
Abstract
The matching hypothesis in social psychology claims that people are more likely to form a committed relationship with someone equally attractive. Previous works on stochastic models of human mate choice process indicate that patterns supporting the matching hypothesis could occur even when similarity is not the primary consideration in seeking partners. Yet, most if not all of these works concentrate on fully-connected systems. Here we extend the analysis to networks. Our results indicate that the correlation of the couple's attractiveness grows monotonically with the increased average degree and decreased degree diversity of the network. This correlation is lower in sparse networks than in fully-connected systems, because in the former less attractive individuals who find partners are likely to be coupled with ones who are more attractive than them. The chance of failing to be matched decreases exponentially with both the attractiveness and the degree. The matching hypothesis may not hold when the degree-attractiveness correlation is present, which can give rise to negative attractiveness correlation. Finally, we find that the ratio between the number of matched couples and the size of the maximum matching varies non-monotonically with the average degree of the network. Our results reveal the role of network topology in the process of human mate choice and bring insights into future investigations of different matching processes in networks.
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Affiliation(s)
- Tao Jia
- Social Cognitive Networks Academic Research Center, Rensselaer Polytechnic Institute, Troy, NY, 12180 USA
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, 12180 USA
- Department of Physics, Applied Physics and Astronomy, Rensselaer Polytechnic Institute, Troy, NY, 12180 USA
- * E-mail:
| | - Robert F. Spivey
- Department of Physics, Applied Physics and Astronomy, Rensselaer Polytechnic Institute, Troy, NY, 12180 USA
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, 27708 USA
| | - Boleslaw Szymanski
- Social Cognitive Networks Academic Research Center, Rensselaer Polytechnic Institute, Troy, NY, 12180 USA
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, 12180 USA
- Społeczna Akademia Nauk, Łódź, Poland
| | - Gyorgy Korniss
- Social Cognitive Networks Academic Research Center, Rensselaer Polytechnic Institute, Troy, NY, 12180 USA
- Department of Physics, Applied Physics and Astronomy, Rensselaer Polytechnic Institute, Troy, NY, 12180 USA
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