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The Physics of Friendship
Source: physorg.com
By comparing people to mobile particles randomly
bouncing off each other, scientists have developed a
new model for social networks. The model fits with
empirical data to naturally reproduce the community
structure, clustering and evolution of general
acquaintances and even sexual contacts.
Applying a mathematical model to the social dynamics
of people presents difficulties not involved with more
physical ? and perhaps more rational ? applications.
The many factors that influence an individual?s fate
to meet an acquaintance and decide to become a friend
are impossible to capture, but physicists have used
techniques from physical systems to model social
networks with near precision.
By modeling people?s interactions based on how
particles bounce off each other in an enclosed area,
physicists Marta Gonzalez, Pedro Lind and Hans
Herrmann found that the characteristics of social
networks emerge ?in a very natural way.? In a study
recently published in Physical Review Letters, the
scientists compared their model to empirical data
taken from a survey of more than 90,000 U.S. students
regarding friendships, and found similarities
indicating that this model may serve as a novel
approach for understanding social networks.
?The idea behind our model, though simple, is
different from the usual paradigmatic approaches,?
Gonzalez told PhysOrg.com. ?We consider a system of
mobile agents (students), which at the beginning have
no acquaintances; by moving in a continuous space they
collide with each other, forming their friendships.?
After a collision, a particle moves in a different
direction with an updated velocity, just as how an
individual?s chance of meeting a new person depends on
their most recent acquaintances.
At a critical point, the system reaches a
quasi-stationary state, for the first time allowing
the scientists to reproduce several features of social
networks in a single model and in a natural way.
Specifically, this technique accurately describes
social clustering, the way friendships evolve over
time, the shortest path length in a large group, and
some features related to group structure.
?With this new framework, we show that specific
velocity and collision rules are able to reproduce the
statistical and structural features of empirical
social networks,? said Gonzalez. ?Therefore, this
model seems to have the novelty of bringing together
all the previous developments for collision theory
with the empirical results of socio-dynamics.?
The scientists were also able to apply this model to
describe specific types of contacts to produce a
distribution that again closely resembles real-life
acquaintances. For example, to separate sexual
contacts from all social contacts, the scientists
assigned to the sexual contacts an intrinsic property
that could then be used to model these distinct
networks. In this case, the model reproduced the real
sexual contact network found in a tracing study of HIV
tests.
Although this particle motion does not literally model
human motion, it represents connections among people ?
and it?s these links that contain the most
significance for social networking theories. For
example, links can represent the flow of information
traveling through a community. By knowing the shortest
path, communicators can optimize the information flow
and improve productivity in a business. With the
ability to determine hot hubs or holes in a community,
business managers can identify leaders or points that
require an organizational change.
As Gonzalez sees it, statistical physics and human
behavioral studies have a history of inspiring each
other, which makes physicists? desire to understand
social networks a natural interest.
?As Philip Ball remarks,? said
Gonzalez, ??by seeking to uncover the rules of
collective human activities, today's statistical
physicists are aiming to return to their roots: Social
statistics also guided Maxwell and Boltzmann towards
the utilization of probability distributions in the
development of the kinetic theory of gases ? the
foundation of statistical mechanics? (Physica A 314
(2002) 1-4)."
Citation: Gonzalez, Marta C., Lind, Pedro G. and
Herrmann, Hans J. System of Mobile Agents to Model
Social Networks. Physical Review Letters. 96, 088701
(2006).
By Lisa Zyga, Copyright 2006 PhysOrg.com
This news is brought to you by PhysOrg.com
By comparing people to mobile particles randomly
bouncing off each other, scientists have developed a
new model for social networks. The model fits with
empirical data to naturally reproduce the community
structure, clustering and evolution of general
acquaintances and even sexual contacts.
Applying a mathematical model to the social dynamics
of people presents difficulties not involved with more
physical ? and perhaps more rational ? applications.
The many factors that influence an individual?s fate
to meet an acquaintance and decide to become a friend
are impossible to capture, but physicists have used
techniques from physical systems to model social
networks with near precision.
By modeling people?s interactions based on how
particles bounce off each other in an enclosed area,
physicists Marta Gonzalez, Pedro Lind and Hans
Herrmann found that the characteristics of social
networks emerge ?in a very natural way.? In a study
recently published in Physical Review Letters, the
scientists compared their model to empirical data
taken from a survey of more than 90,000 U.S. students
regarding friendships, and found similarities
indicating that this model may serve as a novel
approach for understanding social networks.
?The idea behind our model, though simple, is
different from the usual paradigmatic approaches,?
Gonzalez told PhysOrg.com. ?We consider a system of
mobile agents (students), which at the beginning have
no acquaintances; by moving in a continuous space they
collide with each other, forming their friendships.?
After a collision, a particle moves in a different
direction with an updated velocity, just as how an
individual?s chance of meeting a new person depends on
their most recent acquaintances.
At a critical point, the system reaches a
quasi-stationary state, for the first time allowing
the scientists to reproduce several features of social
networks in a single model and in a natural way.
Specifically, this technique accurately describes
social clustering, the way friendships evolve over
time, the shortest path length in a large group, and
some features related to group structure.
?With this new framework, we show that specific
velocity and collision rules are able to reproduce the
statistical and structural features of empirical
social networks,? said Gonzalez. ?Therefore, this
model seems to have the novelty of bringing together
all the previous developments for collision theory
with the empirical results of socio-dynamics.?
The scientists were also able to apply this model to
describe specific types of contacts to produce a
distribution that again closely resembles real-life
acquaintances. For example, to separate sexual
contacts from all social contacts, the scientists
assigned to the sexual contacts an intrinsic property
that could then be used to model these distinct
networks. In this case, the model reproduced the real
sexual contact network found in a tracing study of HIV
tests.
Although this particle motion does not literally model
human motion, it represents connections among people ?
and it?s these links that contain the most
significance for social networking theories. For
example, links can represent the flow of information
traveling through a community. By knowing the shortest
path, communicators can optimize the information flow
and improve productivity in a business. With the
ability to determine hot hubs or holes in a community,
business managers can identify leaders or points that
require an organizational change.
As Gonzalez sees it, statistical physics and human
behavioral studies have a history of inspiring each
other, which makes physicists? desire to understand
social networks a natural interest.
?As Philip Ball remarks,? said
Gonzalez, ??by seeking to uncover the rules of
collective human activities, today's statistical
physicists are aiming to return to their roots: Social
statistics also guided Maxwell and Boltzmann towards
the utilization of probability distributions in the
development of the kinetic theory of gases ? the
foundation of statistical mechanics? (Physica A 314
(2002) 1-4)."
Citation: Gonzalez, Marta C., Lind, Pedro G. and
Herrmann, Hans J. System of Mobile Agents to Model
Social Networks. Physical Review Letters. 96, 088701
(2006).
By Lisa Zyga, Copyright 2006 PhysOrg.com
This news is brought to you by PhysOrg.com
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