617 research outputs found
A framework for community detection in heterogeneous multi-relational networks
There has been a surge of interest in community detection in homogeneous
single-relational networks which contain only one type of nodes and edges.
However, many real-world systems are naturally described as heterogeneous
multi-relational networks which contain multiple types of nodes and edges. In
this paper, we propose a new method for detecting communities in such networks.
Our method is based on optimizing the composite modularity, which is a new
modularity proposed for evaluating partitions of a heterogeneous
multi-relational network into communities. Our method is parameter-free,
scalable, and suitable for various networks with general structure. We
demonstrate that it outperforms the state-of-the-art techniques in detecting
pre-planted communities in synthetic networks. Applied to a real-world Digg
network, it successfully detects meaningful communities.Comment: 27 pages, 10 figure
Motif-based communities in complex networks
Community definitions usually focus on edges, inside and between the
communities. However, the high density of edges within a community determines
correlations between nodes going beyond nearest-neighbours, and which are
indicated by the presence of motifs. We show how motifs can be used to define
general classes of nodes, including communities, by extending the mathematical
expression of Newman-Girvan modularity. We construct then a general framework
and apply it to some synthetic and real networks
Bank-firm credit network in Japan. An analysis of a bipartite network
We present an analysis of the credit market of Japan. The analysis is
performed by investigating the bipartite network of banks and firms which is
obtained by setting a link between a bank and a firm when a credit relationship
is present in a given time window. In our investigation we focus on a community
detection algorithm which is identifying communities composed by both banks and
firms. We show that the clusters obtained by directly working on the bipartite
network carry information about the networked nature of the Japanese credit
market. Our analysis is performed for each calendar year during the time period
from 1980 to 2011. Specifically, we obtain communities of banks and networks
for each of the 32 investigated years, and we introduce a method to track the
time evolution of these communities on a statistical basis. We then
characterize communities by detecting the simultaneous over-expression of
attributes of firms and banks. Specifically, we consider as attributes the
economic sector and the geographical location of firms and the type of banks.
In our 32 year long analysis we detect a persistence of the over-expression of
attributes of clusters of banks and firms together with a slow dynamics of
changes from some specific attributes to new ones. Our empirical observations
show that the credit market in Japan is a networked market where the type of
banks, geographical location of firms and banks and economic sector of the firm
play a role in shaping the credit relationships between banks and firms.Comment: 9 pages, 4 figures, 2 Table
Community detection in multiplex networks using locally adaptive random walks
Multiplex networks, a special type of multilayer networks, are increasingly
applied in many domains ranging from social media analytics to biology. A
common task in these applications concerns the detection of community
structures. Many existing algorithms for community detection in multiplexes
attempt to detect communities which are shared by all layers. In this article
we propose a community detection algorithm, LART (Locally Adaptive Random
Transitions), for the detection of communities that are shared by either some
or all the layers in the multiplex. The algorithm is based on a random walk on
the multiplex, and the transition probabilities defining the random walk are
allowed to depend on the local topological similarity between layers at any
given node so as to facilitate the exploration of communities across layers.
Based on this random walk, a node dissimilarity measure is derived and nodes
are clustered based on this distance in a hierarchical fashion. We present
experimental results using networks simulated under various scenarios to
showcase the performance of LART in comparison to related community detection
algorithms
Community Structure in Time-Dependent, Multiscale, and Multiplex Networks
Network science is an interdisciplinary endeavor, with methods and
applications drawn from across the natural, social, and information sciences. A
prominent problem in network science is the algorithmic detection of
tightly-connected groups of nodes known as communities. We developed a
generalized framework of network quality functions that allowed us to study the
community structure of arbitrary multislice networks, which are combinations of
individual networks coupled through links that connect each node in one network
slice to itself in other slices. This framework allows one to study community
structure in a very general setting encompassing networks that evolve over
time, have multiple types of links (multiplexity), and have multiple scales.Comment: 31 pages, 3 figures, 1 table. Includes main text and supporting
material. This is the accepted version of the manuscript (the definitive
version appeared in Science), with typographical corrections included her
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