3 research outputs found
An Analysis of the Matching Hypothesis in Networks
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
Network resilience
Many systems on our planet are known to shift abruptly and irreversibly from
one state to another when they are forced across a "tipping point," such as
mass extinctions in ecological networks, cascading failures in infrastructure
systems, and social convention changes in human and animal networks. Such a
regime shift demonstrates a system's resilience that characterizes the ability
of a system to adjust its activity to retain its basic functionality in the
face of internal disturbances or external environmental changes. In the past 50
years, attention was almost exclusively given to low dimensional systems and
calibration of their resilience functions and indicators of early warning
signals without considerations for the interactions between the components.
Only in recent years, taking advantages of the network theory and lavish real
data sets, network scientists have directed their interest to the real-world
complex networked multidimensional systems and their resilience function and
early warning indicators. This report is devoted to a comprehensive review of
resilience function and regime shift of complex systems in different domains,
such as ecology, biology, social systems and infrastructure. We cover the
related research about empirical observations, experimental studies,
mathematical modeling, and theoretical analysis. We also discuss some ambiguous
definitions, such as robustness, resilience, and stability.Comment: Review chapter