Not all nodes in a network are created equal. Differences and similarities
exist at both individual node and group levels. Disentangling single node from
group properties is crucial for network modeling and structural inference.
Based on unbiased generative probabilistic exponential random graph models and
employing distributive message passing techniques, we present an efficient
algorithm that allows one to separate the contributions of individual nodes and
groups of nodes to the network structure. This leads to improved detection
accuracy of latent class structure in real world data sets compared to models
that focus on group structure alone. Furthermore, the inclusion of hitherto
neglected group specific effects in models used to assess the statistical
significance of small subgraph (motif) distributions in networks may be
sufficient to explain most of the observed statistics. We show the predictive
power of such generative models in forecasting putative gene-disease
associations in the Online Mendelian Inheritance in Man (OMIM) database. The
approach is suitable for both directed and undirected uni-partite as well as
for bipartite networks.
Description
[1012.4524] The interplay of microscopic and mesoscopic structure in complex networks
%0 Generic
%1 reichardt2010interplay
%A Reichardt, Joerg
%A Alamino, Roberto
%A Saad, David
%D 2010
%K community detection em latent model probabilistic sunbelt
%T The interplay of microscopic and mesoscopic structure in complex
networks
%U http://arxiv.org/abs/1012.4524
%X Not all nodes in a network are created equal. Differences and similarities
exist at both individual node and group levels. Disentangling single node from
group properties is crucial for network modeling and structural inference.
Based on unbiased generative probabilistic exponential random graph models and
employing distributive message passing techniques, we present an efficient
algorithm that allows one to separate the contributions of individual nodes and
groups of nodes to the network structure. This leads to improved detection
accuracy of latent class structure in real world data sets compared to models
that focus on group structure alone. Furthermore, the inclusion of hitherto
neglected group specific effects in models used to assess the statistical
significance of small subgraph (motif) distributions in networks may be
sufficient to explain most of the observed statistics. We show the predictive
power of such generative models in forecasting putative gene-disease
associations in the Online Mendelian Inheritance in Man (OMIM) database. The
approach is suitable for both directed and undirected uni-partite as well as
for bipartite networks.
@misc{reichardt2010interplay,
abstract = { Not all nodes in a network are created equal. Differences and similarities
exist at both individual node and group levels. Disentangling single node from
group properties is crucial for network modeling and structural inference.
Based on unbiased generative probabilistic exponential random graph models and
employing distributive message passing techniques, we present an efficient
algorithm that allows one to separate the contributions of individual nodes and
groups of nodes to the network structure. This leads to improved detection
accuracy of latent class structure in real world data sets compared to models
that focus on group structure alone. Furthermore, the inclusion of hitherto
neglected group specific effects in models used to assess the statistical
significance of small subgraph (motif) distributions in networks may be
sufficient to explain most of the observed statistics. We show the predictive
power of such generative models in forecasting putative gene-disease
associations in the Online Mendelian Inheritance in Man (OMIM) database. The
approach is suitable for both directed and undirected uni-partite as well as
for bipartite networks.
},
added-at = {2011-02-11T20:59:17.000+0100},
author = {Reichardt, Joerg and Alamino, Roberto and Saad, David},
biburl = {https://www.bibsonomy.org/bibtex/2d1cfb30421951a8347ecdbf1a7c060eb/folke},
description = {[1012.4524] The interplay of microscopic and mesoscopic structure in complex networks},
interhash = {880424e7151b1d9d650b2a33b0f595b0},
intrahash = {d1cfb30421951a8347ecdbf1a7c060eb},
keywords = {community detection em latent model probabilistic sunbelt},
note = {cite arxiv:1012.4524
},
timestamp = {2011-03-30T15:25:51.000+0200},
title = {The interplay of microscopic and mesoscopic structure in complex
networks},
url = {http://arxiv.org/abs/1012.4524},
year = 2010
}