D. Conway. (2011)cite arxiv:1105.0902
Comment: 33 pages, 7 pages, GMM code available at:
https://github.com/drewconway/GMM.
Abstract
Network structures are extremely important to the study of political science.
Much of the data in its subfields are naturally represented as networks. This
includes trade, diplomatic and conflict relationships. The social structure of
several organization is also of interest to many researchers, such as the
affiliations of legislators or the relationships among terrorist. A key aspect
of studying social networks is understanding the evolutionary dynamics and the
mechanism by which these structures grow and change over time. While current
methods are well suited to describe static features of networks, they are less
capable of specifying models of change and simulating network evolution. In the
following paper I present a new method for modeling network growth and
evolution. This method relies on graph motifs to generate simulated network
data with particular structural characteristic. This technique departs notably
from current methods both in form and function. Rather than a closed-form
model, or stochastic implementation from a single class of graphs, the proposed
"graph motif model" provides a framework for building flexible and complex
models of network evolution. The paper proceeds as follows: first a brief
review of the current literature on network modeling is provided to place the
graph motif model in context. Next, the graph motif model is introduced, and a
simple example is provided. As a proof of concept, three classic random graph
models are recovered using the graph motif modeling method: the Erdos-Renyi
binomial random graph, the Watts-Strogatz "small world" model, and the
Barabasi-Albert preferential attachment model. In the final section I discuss
the results of these simulations and subsequent advantage and disadvantages
presented by using this technique to model social networks.
%0 Generic
%1 Conway2011
%A Conway, Drew
%D 2011
%K networks sna
%T Modeling Network Evolution Using Graph Motifs
%U http://arxiv.org/abs/1105.0902
%X Network structures are extremely important to the study of political science.
Much of the data in its subfields are naturally represented as networks. This
includes trade, diplomatic and conflict relationships. The social structure of
several organization is also of interest to many researchers, such as the
affiliations of legislators or the relationships among terrorist. A key aspect
of studying social networks is understanding the evolutionary dynamics and the
mechanism by which these structures grow and change over time. While current
methods are well suited to describe static features of networks, they are less
capable of specifying models of change and simulating network evolution. In the
following paper I present a new method for modeling network growth and
evolution. This method relies on graph motifs to generate simulated network
data with particular structural characteristic. This technique departs notably
from current methods both in form and function. Rather than a closed-form
model, or stochastic implementation from a single class of graphs, the proposed
"graph motif model" provides a framework for building flexible and complex
models of network evolution. The paper proceeds as follows: first a brief
review of the current literature on network modeling is provided to place the
graph motif model in context. Next, the graph motif model is introduced, and a
simple example is provided. As a proof of concept, three classic random graph
models are recovered using the graph motif modeling method: the Erdos-Renyi
binomial random graph, the Watts-Strogatz "small world" model, and the
Barabasi-Albert preferential attachment model. In the final section I discuss
the results of these simulations and subsequent advantage and disadvantages
presented by using this technique to model social networks.
@misc{Conway2011,
abstract = { Network structures are extremely important to the study of political science.
Much of the data in its subfields are naturally represented as networks. This
includes trade, diplomatic and conflict relationships. The social structure of
several organization is also of interest to many researchers, such as the
affiliations of legislators or the relationships among terrorist. A key aspect
of studying social networks is understanding the evolutionary dynamics and the
mechanism by which these structures grow and change over time. While current
methods are well suited to describe static features of networks, they are less
capable of specifying models of change and simulating network evolution. In the
following paper I present a new method for modeling network growth and
evolution. This method relies on graph motifs to generate simulated network
data with particular structural characteristic. This technique departs notably
from current methods both in form and function. Rather than a closed-form
model, or stochastic implementation from a single class of graphs, the proposed
"graph motif model" provides a framework for building flexible and complex
models of network evolution. The paper proceeds as follows: first a brief
review of the current literature on network modeling is provided to place the
graph motif model in context. Next, the graph motif model is introduced, and a
simple example is provided. As a proof of concept, three classic random graph
models are recovered using the graph motif modeling method: the Erdos-Renyi
binomial random graph, the Watts-Strogatz "small world" model, and the
Barabasi-Albert preferential attachment model. In the final section I discuss
the results of these simulations and subsequent advantage and disadvantages
presented by using this technique to model social networks.},
added-at = {2011-05-05T15:25:33.000+0200},
author = {Conway, Drew},
biburl = {https://www.bibsonomy.org/bibtex/2b9d6b6628abb0f65104fe324f25af573/enricostano},
description = {Modeling Network Evolution Using Graph Motifs},
interhash = {5caddca26418ea9dea40aa8773064505},
intrahash = {b9d6b6628abb0f65104fe324f25af573},
keywords = {networks sna},
note = {cite arxiv:1105.0902
Comment: 33 pages, 7 pages, GMM code available at:
https://github.com/drewconway/GMM},
timestamp = {2011-05-10T18:33:43.000+0200},
title = {Modeling Network Evolution Using Graph Motifs},
url = {http://arxiv.org/abs/1105.0902},
year = 2011
}