29,069 research outputs found
Finding multiple core-periphery pairs in networks
With a core-periphery structure of networks, core nodes are densely
interconnected, peripheral nodes are connected to core nodes to different
extents, and peripheral nodes are sparsely interconnected. Core-periphery
structure composed of a single core and periphery has been identified for
various networks. However, analogous to the observation that many empirical
networks are composed of densely interconnected groups of nodes, i.e.,
communities, a network may be better regarded as a collection of multiple cores
and peripheries. We propose a scalable algorithm to detect multiple
non-overlapping groups of core-periphery structure in a network. We illustrate
our algorithm using synthesised and empirical networks. For example, we find
distinct core-periphery pairs with different political leanings in a network of
political blogs and separation between international and domestic subnetworks
of airports in some single countries in a world-wide airport network.Comment: 11 figures and 9 tables. MATLAB codes are available at
www.naokimasuda.net/cp_codes.zi
Finding core-periphery structures with node influences
Detecting core-periphery structures is one of the outstanding issues in complex network analysis. Various algorithms can identify core nodes and periphery nodes. Recent advances found that many networks from real-world data can be better modeled with multiple pairs of core-periphery nodes. In this study, we propose to use an influence propagation process to detect multiple pairs of core-periphery nodes. In this framework, we assume each node can emit a certain amount of influence and propagate it through the network. Then we identify nodes with large influences as core nodes, and we utilize a maximum influence chain to construct a node-pairing network to determine core-periphery pairs. This approach can take node interactions into consideration and can reduce noises in finding pairs. Experiments on randomly generated networks and real-world networks confirm the efficiency and accuracy of our algorithm
Structural changes in the interbank market across the financial crisis from multiple core-periphery analysis
Interbank markets are often characterised in terms of a core-periphery
network structure, with a highly interconnected core of banks holding the
market together, and a periphery of banks connected mostly to the core but not
internally. This paradigm has recently been challenged for short time scales,
where interbank markets seem better characterised by a bipartite structure with
more core-periphery connections than inside the core. Using a novel
core-periphery detection method on the eMID interbank market, we enrich this
picture by showing that the network is actually characterised by multiple
core-periphery pairs. Moreover, a transition from core-periphery to bipartite
structures occurs by shortening the temporal scale of data aggregation. We
further show how the global financial crisis transformed the market, in terms
of composition, multiplicity and internal organisation of core-periphery pairs.
By unveiling such a fine-grained organisation and transformation of the
interbank market, our method can find important applications in the
understanding of how distress can propagate over financial networks.Comment: 17 pages, 9 figures, 1 tabl
Detecting Core-Periphery Structures by Surprise
Detecting the presence of mesoscale structures in complex networks is of
primary importance. This is especially true for financial networks, whose
structural organization deeply affects their resilience to events like default
cascades, shocks propagation, etc. Several methods have been proposed, so far,
to detect communities, i.e. groups of nodes whose connectivity is significantly
large. Communities, however do not represent the only kind of mesoscale
structures characterizing real-world networks: other examples are provided by
bow-tie structures, core-periphery structures and bipartite structures. Here we
propose a novel method to detect statistically-signifcant bimodular structures,
i.e. either bipartite or core-periphery ones. It is based on a modification of
the surprise, recently proposed for detecting communities. Our variant allows
for bimodular nodes partitions to be revealed, by letting links to be placed
either 1) within the core part and between the core and the periphery parts or
2) just between the (empty) layers of a bipartite network. From a technical
point of view, this is achieved by employing a multinomial hypergeometric
distribution instead of the traditional (binomial) hypergeometric one; as in
the latter case, this allows a p-value to be assigned to any given
(bi)partition of the nodes. To illustrate the performance of our method, we
report the results of its application to several real-world networks, including
social, economic and financial ones.Comment: 11 pages, 10 figures. Python code freely available at
https://github.com/jeroenvldj/bimodular_surpris
Communities as Well Separated Subgraphs With Cohesive Cores: Identification of Core-Periphery Structures in Link Communities
Communities in networks are commonly considered as highly cohesive subgraphs
which are well separated from the rest of the network. However, cohesion and
separation often cannot be maximized at the same time, which is why a
compromise is sought by some methods. When a compromise is not suitable for the
problem to be solved it might be advantageous to separate the two criteria. In
this paper, we explore such an approach by defining communities as well
separated subgraphs which can have one or more cohesive cores surrounded by
peripheries. We apply this idea to link communities and present an algorithm
for constructing hierarchical core-periphery structures in link communities and
first test results.Comment: 12 pages, 2 figures, submitted version of a paper accepted for the
7th International Conference on Complex Networks and Their Applications,
December 11-13, 2018, Cambridge, UK; revised version at
http://141.20.126.227/~qm/papers
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