7,542 research outputs found
Using Affiliation Networks to Study the Determinants of Multilateral Research Cooperation Some empirical evidence from EU Framework Programs in biotechnology
This paper studies multilateral cooperation networks among organizations and work on a two-mode representation to study the decision to participate in a consortium. Our objective is to explain the underlying processes that give rise to multilateral collaboration networks. Particularly, we are interested in how heterogeneity in organizations' attributes plays a part and in the geographical dimension of this formation process. We use the data on project proposals submitted to the 7th Framework Program (FP) in the area of Life sciences, Biotechnology and Biochemistry for Sustainable Non-Food. We employ exponential random graph models (p* models) (Frank and Strauss, 1986 ; Wasserman and Pattison, 1996) with node attributes (Agneessens et al., 2004), and we make use of extensions for affiliation networks (Wang et al., 2009). These models do not only enable handling variability in consortium sizes but also relax the assumption on tie/triad independence. We obtained some preliminary results indicating institutional types as a source of heterogeneity affecting participation decisions. Also, these initial results point out that organizations take their potential partners' participations in other projects into account in giving their decision ; organizations located in the core European countries tend to participate in the same project ; the tendency to preserve the composition of a consortium across projects and the tendency of organizations with the same institutional type to co-participate are not significant
Random Graph Generator for Bipartite Networks Modeling
The purpose of this article is to introduce a new iterative algorithm with
properties resembling real life bipartite graphs. The algorithm enables us to
generate wide range of random bigraphs, which features are determined by a set
of parameters.We adapt the advances of last decade in unipartite complex
networks modeling to the bigraph setting. This data structure can be observed
in several situations. However, only a few datasets are freely available to
test the algorithms (e.g. community detection, influential nodes
identification, information retrieval) which operate on such data. Therefore,
artificial datasets are needed to enhance development and testing of the
algorithms. We are particularly interested in applying the generator to the
analysis of recommender systems. Therefore, we focus on two characteristics
that, besides simple statistics, are in our opinion responsible for the
performance of neighborhood based collaborative filtering algorithms. The
features are node degree distribution and local clustering coeficient
Comparing Community Structure to Characteristics in Online Collegiate Social Networks
We study the structure of social networks of students by examining the graphs
of Facebook "friendships" at five American universities at a single point in
time. We investigate each single-institution network's community structure and
employ graphical and quantitative tools, including standardized pair-counting
methods, to measure the correlations between the network communities and a set
of self-identified user characteristics (residence, class year, major, and high
school). We review the basic properties and statistics of the pair-counting
indices employed and recall, in simplified notation, a useful analytical
formula for the z-score of the Rand coefficient. Our study illustrates how to
examine different instances of social networks constructed in similar
environments, emphasizes the array of social forces that combine to form
"communities," and leads to comparative observations about online social lives
that can be used to infer comparisons about offline social structures. In our
illustration of this methodology, we calculate the relative contributions of
different characteristics to the community structure of individual universities
and subsequently compare these relative contributions at different
universities, measuring for example the importance of common high school
affiliation to large state universities and the varying degrees of influence
common major can have on the social structure at different universities. The
heterogeneity of communities that we observe indicates that these networks
typically have multiple organizing factors rather than a single dominant one.Comment: Version 3 (17 pages, 5 multi-part figures), accepted in SIAM Revie
Modeling heterogeneity in random graphs through latent space models: a selective review
We present a selective review on probabilistic modeling of heterogeneity in
random graphs. We focus on latent space models and more particularly on
stochastic block models and their extensions that have undergone major
developments in the last five years
Random Networks with Tunable Degree Distribution and Clustering
We present an algorithm for generating random networks with arbitrary degree
distribution and Clustering (frequency of triadic closure). We use this
algorithm to generate networks with exponential, power law, and poisson degree
distributions with variable levels of clustering. Such networks may be used as
models of social networks and as a testable null hypothesis about network
structure. Finally, we explore the effects of clustering on the point of the
phase transition where a giant component forms in a random network, and on the
size of the giant component. Some analysis of these effects is presented.Comment: 9 pages, 13 figures corrected typos, added two references,
reorganized reference
Discourse network analysis: policy debates as dynamic networks
Political discourse is the verbal interaction between political actors. Political actors make normative claims about policies conditional on each other. This renders discourse a dynamic network phenomenon. Accordingly, the structure and dynamics of policy debates can be analyzed with a combination of content analysis and dynamic network analysis. After annotating statements of actors in text sources, networks can be created from these structured data, such as congruence or conflict networks at the actor or concept level, affiliation networks of actors and concept stances, and longitudinal versions of these networks. The resulting network data reveal important properties of a debate, such as the structure of advocacy coalitions or discourse coalitions, polarization and consensus formation, and underlying endogenous processes like popularity, reciprocity, or social balance. The added value of discourse network analysis over survey-based policy network research is that policy processes can be analyzed from a longitudinal perspective. Inferential techniques for understanding the micro-level processes governing political discourse are being developed
Model-based clustering for populations of networks
Until recently obtaining data on populations of networks was typically rare.
However, with the advancement of automatic monitoring devices and the growing
social and scientific interest in networks, such data has become more widely
available. From sociological experiments involving cognitive social structures
to fMRI scans revealing large-scale brain networks of groups of patients, there
is a growing awareness that we urgently need tools to analyse populations of
networks and particularly to model the variation between networks due to
covariates. We propose a model-based clustering method based on mixtures of
generalized linear (mixed) models that can be employed to describe the joint
distribution of a populations of networks in a parsimonious manner and to
identify subpopulations of networks that share certain topological properties
of interest (degree distribution, community structure, effect of covariates on
the presence of an edge, etc.). Maximum likelihood estimation for the proposed
model can be efficiently carried out with an implementation of the EM
algorithm. We assess the performance of this method on simulated data and
conclude with an example application on advice networks in a small business.Comment: The final (published) version of the article can be downloaded for
free (Open Access) from the editor's website (click on the DOI link below
Strategies for online inference of model-based clustering in large and growing networks
In this paper we adapt online estimation strategies to perform model-based
clustering on large networks. Our work focuses on two algorithms, the first
based on the SAEM algorithm, and the second on variational methods. These two
strategies are compared with existing approaches on simulated and real data. We
use the method to decipher the connexion structure of the political websphere
during the US political campaign in 2008. We show that our online EM-based
algorithms offer a good trade-off between precision and speed, when estimating
parameters for mixture distributions in the context of random graphs.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS359 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Variational Inference for Stochastic Block Models from Sampled Data
This paper deals with non-observed dyads during the sampling of a network and
consecutive issues in the inference of the Stochastic Block Model (SBM). We
review sampling designs and recover Missing At Random (MAR) and Not Missing At
Random (NMAR) conditions for the SBM. We introduce variants of the variational
EM algorithm for inferring the SBM under various sampling designs (MAR and
NMAR) all available as an R package. Model selection criteria based on
Integrated Classification Likelihood are derived for selecting both the number
of blocks and the sampling design. We investigate the accuracy and the range of
applicability of these algorithms with simulations. We explore two real-world
networks from ethnology (seed circulation network) and biology (protein-protein
interaction network), where the interpretations considerably depends on the
sampling designs considered
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