131,851 research outputs found
Clustering and the hyperbolic geometry of complex networks
Clustering is a fundamental property of complex networks and it is the
mathematical expression of a ubiquitous phenomenon that arises in various types
of self-organized networks such as biological networks, computer networks or
social networks. In this paper, we consider what is called the global
clustering coefficient of random graphs on the hyperbolic plane. This model of
random graphs was proposed recently by Krioukov et al. as a mathematical model
of complex networks, under the fundamental assumption that hyperbolic geometry
underlies the structure of these networks. We give a rigorous analysis of
clustering and characterize the global clustering coefficient in terms of the
parameters of the model. We show how the global clustering coefficient can be
tuned by these parameters and we give an explicit formula for this function.Comment: 51 pages, 1 figur
Self-similar planar graphs as models for complex networks
In this paper we introduce a family of planar, modular and self-similar
graphs which have small-world and scale-free properties. The main parameters of
this family are comparable to those of networks associated to complex systems,
and therefore the graphs are of interest as mathematical models for these
systems. As the clustering coefficient of the graphs is zero, this family is an
explicit construction that does not match the usual characterization of
hierarchical modular networks, namely that vertices have clustering values
inversely proportional to their degrees.Comment: 10 pages, submitted to 19th International Workshop on Combinatorial
Algorithms (IWOCA 2008
On Graph Stream Clustering with Side Information
Graph clustering becomes an important problem due to emerging applications
involving the web, social networks and bio-informatics. Recently, many such
applications generate data in the form of streams. Clustering massive, dynamic
graph streams is significantly challenging because of the complex structures of
graphs and computational difficulties of continuous data. Meanwhile, a large
volume of side information is associated with graphs, which can be of various
types. The examples include the properties of users in social network
activities, the meta attributes associated with web click graph streams and the
location information in mobile communication networks. Such attributes contain
extremely useful information and has the potential to improve the clustering
process, but are neglected by most recent graph stream mining techniques. In
this paper, we define a unified distance measure on both link structures and
side attributes for clustering. In addition, we propose a novel optimization
framework DMO, which can dynamically optimize the distance metric and make it
adapt to the newly received stream data. We further introduce a carefully
designed statistics SGS(C) which consume constant storage spaces with the
progression of streams. We demonstrate that the statistics maintained are
sufficient for the clustering process as well as the distance optimization and
can be scalable to massive graphs with side attributes. We will present
experiment results to show the advantages of the approach in graph stream
clustering with both links and side information over the baselines.Comment: Full version of SIAM SDM 2013 pape
Clustering and Community Detection in Directed Networks: A Survey
Networks (or graphs) appear as dominant structures in diverse domains,
including sociology, biology, neuroscience and computer science. In most of the
aforementioned cases graphs are directed - in the sense that there is
directionality on the edges, making the semantics of the edges non symmetric.
An interesting feature that real networks present is the clustering or
community structure property, under which the graph topology is organized into
modules commonly called communities or clusters. The essence here is that nodes
of the same community are highly similar while on the contrary, nodes across
communities present low similarity. Revealing the underlying community
structure of directed complex networks has become a crucial and
interdisciplinary topic with a plethora of applications. Therefore, naturally
there is a recent wealth of research production in the area of mining directed
graphs - with clustering being the primary method and tool for community
detection and evaluation. The goal of this paper is to offer an in-depth review
of the methods presented so far for clustering directed networks along with the
relevant necessary methodological background and also related applications. The
survey commences by offering a concise review of the fundamental concepts and
methodological base on which graph clustering algorithms capitalize on. Then we
present the relevant work along two orthogonal classifications. The first one
is mostly concerned with the methodological principles of the clustering
algorithms, while the second one approaches the methods from the viewpoint
regarding the properties of a good cluster in a directed network. Further, we
present methods and metrics for evaluating graph clustering results,
demonstrate interesting application domains and provide promising future
research directions.Comment: 86 pages, 17 figures. Physics Reports Journal (To Appear
Efficiency of Scale-Free Networks: Error and Attack Tolerance
The concept of network efficiency, recently proposed to characterize the
properties of small-world networks, is here used to study the effects of errors
and attacks on scale-free networks. Two different kinds of scale-free networks,
i.e. networks with power law P(k), are considered: 1) scale-free networks with
no local clustering produced by the Barabasi-Albert model and 2) scale-free
networks with high clustering properties as in the model by Klemm and Eguiluz,
and their properties are compared to the properties of random graphs
(exponential graphs). By using as mathematical measures the global and the
local efficiency we investigate the effects of errors and attacks both on the
global and the local properties of the network. We show that the global
efficiency is a better measure than the characteristic path length to describe
the response of complex networks to external factors. We find that, at variance
with random graphs, scale-free networks display, both on a global and on a
local scale, a high degree of error tolerance and an extreme vulnerability to
attacks. In fact, the global and the local efficiency are unaffected by the
failure of some randomly chosen nodes, though they are extremely sensititive to
the removal of the few nodes which play a crucial role in maintaining the
network's connectivity.Comment: 23 pages, 10 figure
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