125,006 research outputs found
Inter-similarity between coupled networks
Recent studies have shown that a system composed from several randomly
interdependent networks is extremely vulnerable to random failure. However,
real interdependent networks are usually not randomly interdependent, rather a
pair of dependent nodes are coupled according to some regularity which we coin
inter-similarity. For example, we study a system composed from an
interdependent world wide port network and a world wide airport network and
show that well connected ports tend to couple with well connected airports. We
introduce two quantities for measuring the level of inter-similarity between
networks (i) Inter degree-degree correlation (IDDC) (ii) Inter-clustering
coefficient (ICC). We then show both by simulation models and by analyzing the
port-airport system that as the networks become more inter-similar the system
becomes significantly more robust to random failure.Comment: 4 pages, 3 figure
Coupled node similarity learning for community detection in attributed networks
© 2018 by the authors. Attributed networks consist of not only a network structure but also node attributes. Most existing community detection algorithms only focus on network structures and ignore node attributes, which are also important. Although some algorithms using both node attributes and network structure information have been proposed in recent years, the complex hierarchical coupling relationships within and between attributes, nodes and network structure have not been considered. Such hierarchical couplings are driving factors in community formation. This paper introduces a novel coupled node similarity (CNS) to involve and learn attribute and structure couplings and compute the similarity within and between nodes with categorical attributes in a network. CNS learns and integrates the frequency-based intra-attribute coupled similarity within an attribute, the co-occurrence-based inter-attribute coupled similarity between attributes, and coupled attribute-to-structure similarity based on the homophily property. CNS is then used to generate the weights of edges and transfer a plain graph to a weighted graph. Clustering algorithms detect community structures that are topologically well-connected and semantically coherent on the weighted graphs. Extensive experiments verify the effectiveness of CNS-based community detection algorithms on several data sets by comparing with the state-of-the-art node similarity measures, whether they involve node attribute information and hierarchical interactions, and on various levels of network structure complexity
Wrestling Model of the Repertoire of Activity Propagation Modes in Quadruple Neural Networks
The spontaneous activity of engineered quadruple cultured neural networks (of four-coupled sub-networks) exhibits a repertoire of different types of mutual synchronization events. Each event corresponds to a specific activity propagation mode (APM) defined by the order of activity propagation between the sub-networks. We statistically characterized the frequency of spontaneous appearance of the different types of APMs. The relative frequencies of the APMs were then examined for their power-law properties. We found that the frequencies of appearance of the leading (most frequent) APMs have close to constant algebraic ratio reminiscent of Zipf's scaling of words. We show that the observations are consistent with a simplified “wrestling” model. This model represents an extension of the “boxing arena” model which was previously proposed to describe the ratio between the two activity modes in two coupled sub-networks. The additional new element in the “wrestling” model presented here is that the firing within each network is modeled by a time interval generator with similar intra-network Lévy distribution. We modeled the different burst-initiation zones’ interaction by competition between the stochastic generators with Gaussian inter-network variability. Estimation of the model parameters revealed similarity across different cultures while the inter-burst-interval of the cultures was similar across different APMs as numerical simulation of the model predicts
Impact of lag information on network inference
Extracting useful information from data is a fundamental challenge across
disciplines as diverse as climate, neuroscience, genetics, and ecology. In the
era of ``big data'', data is ubiquitous, but appropriated methods are needed
for gaining reliable information from the data. In this work we consider a
complex system, composed by interacting units, and aim at inferring which
elements influence each other, directly from the observed data. The only
assumption about the structure of the system is that it can be modeled by a
network composed by a set of units connected with un-weighted and
un-directed links, however, the structure of the connections is not known. In
this situation the inference of the underlying network is usually done by using
interdependency measures, computed from the output signals of the units. We
show, using experimental data recorded from randomly coupled electronic
R{\"o}ssler chaotic oscillators, that the information of the lag times obtained
from bivariate cross-correlation analysis can be useful to gain information
about the real connectivity of the system
Community structure in real-world networks from a non-parametrical synchronization-based dynamical approach
This work analyzes the problem of community structure in real-world networks
based on the synchronization of nonidentical coupled chaotic R\"{o}ssler
oscillators each one characterized by a defined natural frequency, and coupled
according to a predefined network topology. The interaction scheme contemplates
an uniformly increasing coupling force to simulate a society in which the
association between the agents grows in time. To enhance the stability of the
correlated states that could emerge from the synchronization process, we
propose a parameterless mechanism that adapts the characteristic frequencies of
coupled oscillators according to a dynamic connectivity matrix deduced from
correlated data. We show that the characteristic frequency vector that results
from the adaptation mechanism reveals the underlying community structure
present in the network.Comment: 21 pages, 7 figures; Chaos, Solitons & Fractals (2012
The Impact of Network Flows on Community Formation in Models of Opinion Dynamics
We study dynamics of opinion formation in a network of coupled agents. As the
network evolves to a steady state, opinions of agents within the same community
converge faster than those of other agents. This framework allows us to study
how network topology and network flow, which mediates the transfer of opinions
between agents, both affect the formation of communities. In traditional models
of opinion dynamics, agents are coupled via conservative flows, which result in
one-to-one opinion transfer. However, social interactions are often
non-conservative, resulting in one-to-many transfer of opinions. We study
opinion formation in networks using one-to-one and one-to-many interactions and
show that they lead to different community structure within the same network.Comment: accepted for publication in The Journal of Mathematical Sociology.
arXiv admin note: text overlap with arXiv:1201.238
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