34,020 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
Detecting Cohesive and 2-mode Communities in Directed and Undirected Networks
Networks are a general language for representing relational information among
objects. An effective way to model, reason about, and summarize networks, is to
discover sets of nodes with common connectivity patterns. Such sets are
commonly referred to as network communities. Research on network community
detection has predominantly focused on identifying communities of densely
connected nodes in undirected networks.
In this paper we develop a novel overlapping community detection method that
scales to networks of millions of nodes and edges and advances research along
two dimensions: the connectivity structure of communities, and the use of edge
directedness for community detection. First, we extend traditional definitions
of network communities by building on the observation that nodes can be densely
interlinked in two different ways: In cohesive communities nodes link to each
other, while in 2-mode communities nodes link in a bipartite fashion, where
links predominate between the two partitions rather than inside them. Our
method successfully detects both 2-mode as well as cohesive communities, that
may also overlap or be hierarchically nested. Second, while most existing
community detection methods treat directed edges as though they were
undirected, our method accounts for edge directions and is able to identify
novel and meaningful community structures in both directed and undirected
networks, using data from social, biological, and ecological domains.Comment: Published in the proceedings of WSDM '1
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