35 research outputs found
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
Extension of Modularity Density for Overlapping Community Structure
Modularity is widely used to effectively measure the strength of the disjoint
community structure found by community detection algorithms. Although several
overlapping extensions of modularity were proposed to measure the quality of
overlapping community structure, there is lack of systematic comparison of
different extensions. To fill this gap, we overview overlapping extensions of
modularity to select the best. In addition, we extend the Modularity Density
metric to enable its usage for overlapping communities. The experimental
results on four real networks using overlapping extensions of modularity,
overlapping modularity density, and six other community quality metrics show
that the best results are obtained when the product of the belonging
coefficients of two nodes is used as the belonging function. Moreover, our
experiments indicate that overlapping modularity density is a better measure of
the quality of overlapping community structure than other metrics considered.Comment: 8 pages in Advances in Social Networks Analysis and Mining (ASONAM),
2014 IEEE/ACM International Conference o
Node-Centric Detection of Overlapping Communities in Social Networks
We present NECTAR, a community detection algorithm that generalizes Louvain
method's local search heuristic for overlapping community structures. NECTAR
chooses dynamically which objective function to optimize based on the network
on which it is invoked. Our experimental evaluation on both synthetic benchmark
graphs and real-world networks, based on ground-truth communities, shows that
NECTAR provides excellent results as compared with state of the art community
detection algorithms
Dual adjacency matrix : exploring link groups in dense networks
Node grouping is a common way of adding structure and information to networks that aids their interpretation. However, certain networks benefit from the grouping of links instead of nodes. Link communities, for example, are a form of link groups that describe high-quality overlapping node communities. There is a conceptual gap between node groups and link groups that poses an interesting visualization challenge. We introduce the Dual Adjacency Matrix to bridge this gap. This matrix combines node and link group techniques via a generalization that also enables it to be coordinated with a node-link-contour diagram. These methods have been implemented in a prototype that we evaluated with an information scientist and neuroscientist via interviews and prototype walk-throughs. We demonstrate this prototype with the analysis of a trade network and an fMRI correlation network
Universality of Performance Indicators based on Citation and Reference Counts
We find evidence for the universality of two relative bibliometric indicators
of the quality of individual scientific publications taken from different data
sets. One of these is a new index that considers both citation and reference
counts. We demonstrate this universality for relatively well cited publications
from a single institute, grouped by year of publication and by faculty or by
department. We show similar behaviour in publications submitted to the arXiv
e-print archive, grouped by year of submission and by sub-archive. We also find
that for reasonably well cited papers this distribution is well fitted by a
lognormal with a variance of around 1.3 which is consistent with the results of
Radicchi, Fortunato, and Castellano (2008). Our work demonstrates that
comparisons can be made between publications from different disciplines and
publication dates, regardless of their citation count and without expensive
access to the whole world-wide citation graph. Further, it shows that averages
of the logarithm of such relative bibliometric indices deal with the issue of
long tails and avoid the need for statistics based on lengthy ranking
procedures.Comment: 15 pages, 14 figures, 11 pages of supplementary material. Submitted
to Scientometric