18,599 research outputs found
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
Complex Networks and Symmetry II: Reciprocity and Evolution of World Trade
We exploit the symmetry concepts developed in the companion review of this
article to introduce a stochastic version of link reversal symmetry, which
leads to an improved understanding of the reciprocity of directed networks. We
apply our formalism to the international trade network and show that a strong
embedding in economic space determines particular symmetries of the network,
while the observed evolution of reciprocity is consistent with a symmetry
breaking taking place in production space. Our results show that networks can
be strongly affected by symmetry-breaking phenomena occurring in embedding
spaces, and that stochastic network symmetries can successfully suggest, or
rule out, possible underlying mechanisms.Comment: Final accepted versio
Analytical maximum-likelihood method to detect patterns in real networks
In order to detect patterns in real networks, randomized graph ensembles that
preserve only part of the topology of an observed network are systematically
used as fundamental null models. However, their generation is still
problematic. The existing approaches are either computationally demanding and
beyond analytic control, or analytically accessible but highly approximate.
Here we propose a solution to this long-standing problem by introducing an
exact and fast method that allows to obtain expectation values and standard
deviations of any topological property analytically, for any binary, weighted,
directed or undirected network. Remarkably, the time required to obtain the
expectation value of any property is as short as that required to compute the
same property on the single original network. Our method reveals that the null
behavior of various correlation properties is different from what previously
believed, and highly sensitive to the particular network considered. Moreover,
our approach shows that important structural properties (such as the modularity
used in community detection problems) are currently based on incorrect
expressions, and provides the exact quantities that should replace them.Comment: 26 pages, 10 figure
Evolution of networks
We review the recent fast progress in statistical physics of evolving
networks. Interest has focused mainly on the structural properties of random
complex networks in communications, biology, social sciences and economics. A
number of giant artificial networks of such a kind came into existence
recently. This opens a wide field for the study of their topology, evolution,
and complex processes occurring in them. Such networks possess a rich set of
scaling properties. A number of them are scale-free and show striking
resilience against random breakdowns. In spite of large sizes of these
networks, the distances between most their vertices are short -- a feature
known as the ``small-world'' effect. We discuss how growing networks
self-organize into scale-free structures and the role of the mechanism of
preferential linking. We consider the topological and structural properties of
evolving networks, and percolation in these networks. We present a number of
models demonstrating the main features of evolving networks and discuss current
approaches for their simulation and analytical study. Applications of the
general results to particular networks in Nature are discussed. We demonstrate
the generic connections of the network growth processes with the general
problems of non-equilibrium physics, econophysics, evolutionary biology, etc.Comment: 67 pages, updated, revised, and extended version of review, submitted
to Adv. Phy
Spectral centrality measures in complex networks
Complex networks are characterized by heterogeneous distributions of the
degree of nodes, which produce a large diversification of the roles of the
nodes within the network. Several centrality measures have been introduced to
rank nodes based on their topological importance within a graph. Here we review
and compare centrality measures based on spectral properties of graph matrices.
We shall focus on PageRank, eigenvector centrality and the hub/authority scores
of HITS. We derive simple relations between the measures and the (in)degree of
the nodes, in some limits. We also compare the rankings obtained with different
centrality measures.Comment: 11 pages, 10 figures, 5 tables. Final version published in Physical
Review
- …