3,756 research outputs found
Modularity clustering is force-directed layout
Two natural and widely used representations for the community structure of
networks are clusterings, which partition the vertex set into disjoint subsets,
and layouts, which assign the vertices to positions in a metric space. This
paper unifies prominent characterizations of layout quality and clustering
quality, by showing that energy models of pairwise attraction and repulsion
subsume Newman and Girvan's modularity measure. Layouts with optimal energy are
relaxations of, and are thus consistent with, clusterings with optimal
modularity, which is of practical relevance because both representations are
complementary and often used together.Comment: 9 pages, 7 figures, see http://code.google.com/p/linloglayout/ for
downloading the graph clustering and layout softwar
Optimizing an Organized Modularity Measure for Topographic Graph Clustering: a Deterministic Annealing Approach
This paper proposes an organized generalization of Newman and Girvan's
modularity measure for graph clustering. Optimized via a deterministic
annealing scheme, this measure produces topologically ordered graph clusterings
that lead to faithful and readable graph representations based on clustering
induced graphs. Topographic graph clustering provides an alternative to more
classical solutions in which a standard graph clustering method is applied to
build a simpler graph that is then represented with a graph layout algorithm. A
comparative study on four real world graphs ranging from 34 to 1 133 vertices
shows the interest of the proposed approach with respect to classical solutions
and to self-organizing maps for graphs
Persistent Homology Guided Force-Directed Graph Layouts
Graphs are commonly used to encode relationships among entities, yet their
abstractness makes them difficult to analyze. Node-link diagrams are popular
for drawing graphs, and force-directed layouts provide a flexible method for
node arrangements that use local relationships in an attempt to reveal the
global shape of the graph. However, clutter and overlap of unrelated structures
can lead to confusing graph visualizations. This paper leverages the persistent
homology features of an undirected graph as derived information for interactive
manipulation of force-directed layouts. We first discuss how to efficiently
extract 0-dimensional persistent homology features from both weighted and
unweighted undirected graphs. We then introduce the interactive persistence
barcode used to manipulate the force-directed graph layout. In particular, the
user adds and removes contracting and repulsing forces generated by the
persistent homology features, eventually selecting the set of persistent
homology features that most improve the layout. Finally, we demonstrate the
utility of our approach across a variety of synthetic and real datasets
A statistical network analysis of the HIV/AIDS epidemics in Cuba
The Cuban contact-tracing detection system set up in 1986 allowed the
reconstruction and analysis of the sexual network underlying the epidemic
(5,389 vertices and 4,073 edges, giant component of 2,386 nodes and 3,168
edges), shedding light onto the spread of HIV and the role of contact-tracing.
Clustering based on modularity optimization provides a better visualization and
understanding of the network, in combination with the study of covariates. The
graph has a globally low but heterogeneous density, with clusters of high
intraconnectivity but low interconnectivity. Though descriptive, our results
pave the way for incorporating structure when studying stochastic SIR epidemics
spreading on social networks
A unified approach to mapping and clustering of bibliometric networks
In the analysis of bibliometric networks, researchers often use mapping and
clustering techniques in a combined fashion. Typically, however, mapping and
clustering techniques that are used together rely on very different ideas and
assumptions. We propose a unified approach to mapping and clustering of
bibliometric networks. We show that the VOS mapping technique and a weighted
and parameterized variant of modularity-based clustering can both be derived
from the same underlying principle. We illustrate our proposed approach by
producing a combined mapping and clustering of the most frequently cited
publications that appeared in the field of information science in the period
1999-2008
Visual Mining of Epidemic Networks
We show how an interactive graph visualization method based on maximal
modularity clustering can be used to explore a large epidemic network. The
visual representation is used to display statistical tests results that expose
the relations between the propagation of HIV in a sexual contact network and
the sexual orientation of the patients.Comment: 8 page
Mining a medieval social network by kernel SOM and related methods
This paper briefly presents several ways to understand the organization of a
large social network (several hundreds of persons). We compare approaches
coming from data mining for clustering the vertices of a graph (spectral
clustering, self-organizing algorithms. . .) and provide methods for
representing the graph from these analysis. All these methods are illustrated
on a medieval social network and the way they can help to understand its
organization is underlined
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