5,796 research outputs found
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
Graphical Analysis of Social Group Dynamics
Identifying communities in social networks becomes an increasingly important
research problem. Several methods for identifying such groups have been
developed, however, qualitative analysis (taking into account the scale of the
problem) still poses serious problems. This paper describes a tool for
facilitating such an analysis, allowing to visualize the dynamics and
supporting localization of different events (such as creation or merging of
groups). In the final part of the paper, the experimental results performed
using the benchmark data (Enron emails) provide an insight into usefulness of
the proposed tool.Comment: Fourth International Conference on Computational Aspects of Social
Networks, CASoN 2012, Sao Carlos, Brazil, November 21-23, 2012, pp. 41-46;
IEEE Computer Society, 201
Element-centric clustering comparison unifies overlaps and hierarchy
Clustering is one of the most universal approaches for understanding complex
data. A pivotal aspect of clustering analysis is quantitatively comparing
clusterings; clustering comparison is the basis for many tasks such as
clustering evaluation, consensus clustering, and tracking the temporal
evolution of clusters. In particular, the extrinsic evaluation of clustering
methods requires comparing the uncovered clusterings to planted clusterings or
known metadata. Yet, as we demonstrate, existing clustering comparison measures
have critical biases which undermine their usefulness, and no measure
accommodates both overlapping and hierarchical clusterings. Here we unify the
comparison of disjoint, overlapping, and hierarchically structured clusterings
by proposing a new element-centric framework: elements are compared based on
the relationships induced by the cluster structure, as opposed to the
traditional cluster-centric philosophy. We demonstrate that, in contrast to
standard clustering similarity measures, our framework does not suffer from
critical biases and naturally provides unique insights into how the clusterings
differ. We illustrate the strengths of our framework by revealing new insights
into the organization of clusters in two applications: the improved
classification of schizophrenia based on the overlapping and hierarchical
community structure of fMRI brain networks, and the disentanglement of various
social homophily factors in Facebook social networks. The universality of
clustering suggests far-reaching impact of our framework throughout all areas
of science
Link communities reveal multiscale complexity in networks
Networks have become a key approach to understanding systems of interacting
objects, unifying the study of diverse phenomena including biological organisms
and human society. One crucial step when studying the structure and dynamics of
networks is to identify communities: groups of related nodes that correspond to
functional subunits such as protein complexes or social spheres. Communities in
networks often overlap such that nodes simultaneously belong to several groups.
Meanwhile, many networks are known to possess hierarchical organization, where
communities are recursively grouped into a hierarchical structure. However, the
fact that many real networks have communities with pervasive overlap, where
each and every node belongs to more than one group, has the consequence that a
global hierarchy of nodes cannot capture the relationships between overlapping
groups. Here we reinvent communities as groups of links rather than nodes and
show that this unorthodox approach successfully reconciles the antagonistic
organizing principles of overlapping communities and hierarchy. In contrast to
the existing literature, which has entirely focused on grouping nodes, link
communities naturally incorporate overlap while revealing hierarchical
organization. We find relevant link communities in many networks, including
major biological networks such as protein-protein interaction and metabolic
networks, and show that a large social network contains hierarchically
organized community structures spanning inner-city to regional scales while
maintaining pervasive overlap. Our results imply that link communities are
fundamental building blocks that reveal overlap and hierarchical organization
in networks to be two aspects of the same phenomenon.Comment: Main text and supplementary informatio
Finding Statistically Significant Communities in Networks
Community structure is one of the main structural features of networks, revealing
both their internal organization and the similarity of their elementary units.
Despite the large variety of methods proposed to detect communities in graphs,
there is a big need for multi-purpose techniques, able to handle different types
of datasets and the subtleties of community structure. In this paper we present
OSLOM (Order Statistics Local Optimization Method), the first method capable to
detect clusters in networks accounting for edge directions, edge weights,
overlapping communities, hierarchies and community dynamics. It is based on the
local optimization of a fitness function expressing the statistical significance
of clusters with respect to random fluctuations, which is estimated with tools
of Extreme and Order Statistics. OSLOM can be used alone or as a refinement
procedure of partitions/covers delivered by other techniques. We have also
implemented sequential algorithms combining OSLOM with other fast techniques, so
that the community structure of very large networks can be uncovered. Our method
has a comparable performance as the best existing algorithms on artificial
benchmark graphs. Several applications on real networks are shown as well. OSLOM
is implemented in a freely available software (http://www.oslom.org), and we
believe it will be a valuable tool in the analysis of networks
Ordered community structure in networks
Community structure in networks is often a consequence of homophily, or
assortative mixing, based on some attribute of the vertices. For example,
researchers may be grouped into communities corresponding to their research
topic. This is possible if vertex attributes have discrete values, but many
networks exhibit assortative mixing by some continuous-valued attribute, such
as age or geographical location. In such cases, no discrete communities can be
identified. We consider how the notion of community structure can be
generalized to networks that are based on continuous-valued attributes: in
general, a network may contain discrete communities which are ordered according
to their attribute values. We propose a method of generating synthetic ordered
networks and investigate the effect of ordered community structure on the
spread of infectious diseases. We also show that community detection algorithms
fail to recover community structure in ordered networks, and evaluate an
alternative method using a layout algorithm to recover the ordering.Comment: This is an extended preprint version that includes an extra example:
the college football network as an ordered (spatial) network. Further
improvements, not included here, appear in the journal version. Original
title changed (from "Ordered and continuous community structure in networks")
to match journal versio
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