96,945 research outputs found
Information dynamics algorithm for detecting communities in networks
The problem of community detection is relevant in many scientific
disciplines, from social science to statistical physics. Given the impact of
community detection in many areas, such as psychology and social sciences, we
have addressed the issue of modifying existing well performing algorithms by
incorporating elements of the domain application fields, i.e. domain-inspired.
We have focused on a psychology and social network - inspired approach which
may be useful for further strengthening the link between social network studies
and mathematics of community detection. Here we introduce a community-detection
algorithm derived from the van Dongen's Markov Cluster algorithm (MCL) method
by considering networks' nodes as agents capable to take decisions. In this
framework we have introduced a memory factor to mimic a typical human behavior
such as the oblivion effect. The method is based on information diffusion and
it includes a non-linear processing phase. We test our method on two classical
community benchmark and on computer generated networks with known community
structure. Our approach has three important features: the capacity of detecting
overlapping communities, the capability of identifying communities from an
individual point of view and the fine tuning the community detectability with
respect to prior knowledge of the data. Finally we discuss how to use a Shannon
entropy measure for parameter estimation in complex networks.Comment: Submitted to "Communication in Nonlinear Science and Numerical
Simulation
Analysis of group evolution prediction in complex networks
In the world, in which acceptance and the identification with social
communities are highly desired, the ability to predict evolution of groups over
time appears to be a vital but very complex research problem. Therefore, we
propose a new, adaptable, generic and mutli-stage method for Group Evolution
Prediction (GEP) in complex networks, that facilitates reasoning about the
future states of the recently discovered groups. The precise GEP modularity
enabled us to carry out extensive and versatile empirical studies on many
real-world complex / social networks to analyze the impact of numerous setups
and parameters like time window type and size, group detection method,
evolution chain length, prediction models, etc. Additionally, many new
predictive features reflecting the group state at a given time have been
identified and tested. Some other research problems like enriching learning
evolution chains with external data have been analyzed as well
Identifying communities by influence dynamics in social networks
Communities are not static; they evolve, split and merge, appear and
disappear, i.e. they are product of dynamical processes that govern the
evolution of the network. A good algorithm for community detection should not
only quantify the topology of the network, but incorporate the dynamical
processes that take place on the network. We present a novel algorithm for
community detection that combines network structure with processes that support
creation and/or evolution of communities. The algorithm does not embrace the
universal approach but instead tries to focus on social networks and model
dynamic social interactions that occur on those networks. It identifies
leaders, and communities that form around those leaders. It naturally supports
overlapping communities by associating each node with a membership vector that
describes node's involvement in each community. This way, in addition to
overlapping communities, we can identify nodes that are good followers to their
leader, and also nodes with no clear community involvement that serve as a
proxy between several communities and are equally as important. We run the
algorithm for several real social networks which we believe represent a good
fraction of the wide body of social networks and discuss the results including
other possible applications.Comment: 10 pages, 6 figure
Bayesian stochastic blockmodeling
This chapter provides a self-contained introduction to the use of Bayesian
inference to extract large-scale modular structures from network data, based on
the stochastic blockmodel (SBM), as well as its degree-corrected and
overlapping generalizations. We focus on nonparametric formulations that allow
their inference in a manner that prevents overfitting, and enables model
selection. We discuss aspects of the choice of priors, in particular how to
avoid underfitting via increased Bayesian hierarchies, and we contrast the task
of sampling network partitions from the posterior distribution with finding the
single point estimate that maximizes it, while describing efficient algorithms
to perform either one. We also show how inferring the SBM can be used to
predict missing and spurious links, and shed light on the fundamental
limitations of the detectability of modular structures in networks.Comment: 44 pages, 16 figures. Code is freely available as part of graph-tool
at https://graph-tool.skewed.de . See also the HOWTO at
https://graph-tool.skewed.de/static/doc/demos/inference/inference.htm
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