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An Analysis of the Matching Hypothesis in Networks.

Jia T, Spivey RF, Szymanski B, Korniss G - PLoS ONE (2015)

Bottom Line: 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.The chance of failing to be matched decreases exponentially with both the attractiveness and the degree.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.

View Article: PubMed Central - PubMed

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.

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.

No MeSH data available.


(a) An example of a bipartite graph, which is composed of two disjoint sets of nodes m and f. There is no link between nodes in the same set and the connection between sets is characterized by degree distribution P(k). (b) The action scheme of the mate choosing process. Two nodes i and j have to undergo an intermediate stage to reach the stable long term relation. During the intermediate stage nodes i and j are also available to build relationship with other nodes. If this happens they break and their relationship is back to the initial state.
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pone.0129804.g001: (a) An example of a bipartite graph, which is composed of two disjoint sets of nodes m and f. There is no link between nodes in the same set and the connection between sets is characterized by degree distribution P(k). (b) The action scheme of the mate choosing process. Two nodes i and j have to undergo an intermediate stage to reach the stable long term relation. During the intermediate stage nodes i and j are also available to build relationship with other nodes. If this happens they break and their relationship is back to the initial state.

Mentions: We start with a bipartite graph with 2N nodes. The bipartite graph consists of two disjoint sets m and f of equal size, representing two parties, each with N members. While our model can be more general, for simplicity, we consider the two parties as collections of heterosexual male and female individuals (Fig 1a). Each node, representing one individual, has k links drawn from the degree distribution P(k), randomly connecting to k nodes in the other set. On average, a node has ⟨k⟩ = ∑kP(k) links, referred to the average degree of the network. To characterize the process of human mate choice, each node is assigned a random number a as its attractiveness drawn uniformly from the range [0,1). Combining features in some previous works [5, 8] with the network structure, we consider the process of human mate choice as a two-step stochastic process which generates the numerical model as follows (Fig 1b):


An Analysis of the Matching Hypothesis in Networks.

Jia T, Spivey RF, Szymanski B, Korniss G - PLoS ONE (2015)

(a) An example of a bipartite graph, which is composed of two disjoint sets of nodes m and f. There is no link between nodes in the same set and the connection between sets is characterized by degree distribution P(k). (b) The action scheme of the mate choosing process. Two nodes i and j have to undergo an intermediate stage to reach the stable long term relation. During the intermediate stage nodes i and j are also available to build relationship with other nodes. If this happens they break and their relationship is back to the initial state.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4470921&req=5

pone.0129804.g001: (a) An example of a bipartite graph, which is composed of two disjoint sets of nodes m and f. There is no link between nodes in the same set and the connection between sets is characterized by degree distribution P(k). (b) The action scheme of the mate choosing process. Two nodes i and j have to undergo an intermediate stage to reach the stable long term relation. During the intermediate stage nodes i and j are also available to build relationship with other nodes. If this happens they break and their relationship is back to the initial state.
Mentions: We start with a bipartite graph with 2N nodes. The bipartite graph consists of two disjoint sets m and f of equal size, representing two parties, each with N members. While our model can be more general, for simplicity, we consider the two parties as collections of heterosexual male and female individuals (Fig 1a). Each node, representing one individual, has k links drawn from the degree distribution P(k), randomly connecting to k nodes in the other set. On average, a node has ⟨k⟩ = ∑kP(k) links, referred to the average degree of the network. To characterize the process of human mate choice, each node is assigned a random number a as its attractiveness drawn uniformly from the range [0,1). Combining features in some previous works [5, 8] with the network structure, we consider the process of human mate choice as a two-step stochastic process which generates the numerical model as follows (Fig 1b):

Bottom Line: 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.The chance of failing to be matched decreases exponentially with both the attractiveness and the degree.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.

View Article: PubMed Central - PubMed

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.

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.

No MeSH data available.