title: An Analysis of the Matching Hypothesis in Networks.
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.
authors.affiliation: 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.
authors.forename: Tao
authors.initials: T
authors.lastname: Jia
authors.affiliation: 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.
authors.forename: Robert F
authors.initials: RF
authors.lastname: Spivey
authors.affiliation: 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.
authors.forename: Boleslaw
authors.initials: B
authors.lastname: Szymanski
authors.affiliation: 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.
authors.forename: Gyorgy
authors.initials: G
authors.lastname: Korniss
mesh_terms: D003196:Computer Graphics; D005191:Family Characteristics; D005260:Female; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D011336:Probability; D012747:Sexual Partners