Multilayer network decoding versatility and trust

Sarkar, Camellia; Yadav, Alok; Jalan, Sarika

doi:10.1209/0295-5075/113/18007

Cited by 28 publications

(22 citation statements)

References 50 publications

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“…To assess the functional importance of these weak ties, we investigated the characteristic properties of the end nodes of these weak ties. Unlike social networks where the end nodes of weak ties are low degree nodes (Sarkar et al 2016), in the sporulation networks of SK1, the nodes forming weak ties were high degree nodes. An example of this was BAS1, a Mybrelated transcription factor involved in amino acid metabolism and meiosis (Mieczkowski et al 2006).…”

Section: Local Properties Of Longitudinal Regulatory Network Of Sk1 mentioning

confidence: 98%

Longitudinal network theory approaches identify crucial factors affecting sporulation efficiency in yeast

Sarkar

Gupta

Verma

et al. 2016

Preprint

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Integrating network theory approaches over longitudinal genome-wide gene expression data is a robust approach to understand the molecular underpinnings of a dynamic biological process. Here, we performed a network-based investigation of longitudinal gene expression changes during sporulation of a yeast strain, SK1. Using global network attributes, viz.clustering coefficient, degree distribution of a node, degree-degree mixing of the connected nodes and disassortativity, we observed dynamic changes in these parameters indicating a highly connected network with inter-module crosstalk. Analysis of local attributes, such as clustering coefficient, hierarchy, betweenness centrality and Granovetter's weak ties showed that there was an inherent hierarchy under regulatory control that was determined by specific nodes. Biological annotation of these nodes indicated the role of specifically linked pairs of genes in meiosis. These genes act as crucial regulators of sporulation in the highly sporulating SK1 strain. An independent analysis of these network properties in a less efficient sporulating strain helped to understand the heterogeneity of network profiles. We show that comparison of network properties has the potential to identify candidate nodes contributing to the phenotypic diversity of developmental processes in natural populations. Therefore, studying these network parameters as described in this work for dynamic developmental processes, such as sporulation in yeast and eventually in disease progression in humans, can help in identifying candidate factors which are potential regulators of differences between normal and perturbed processes and can be causal targets for intervention.

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Section: Local Properties Of Longitudinal Regulatory Network Of Sk1 mentioning

confidence: 98%

Longitudinal network theory approaches identify crucial factors affecting sporulation efficiency in yeast

Sarkar

Gupta

Verma

et al. 2016

Preprint

Self Cite

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“…It has also been shown that introduction of weights enhances the complete synchronization of identical dynamical units in scale-free networks [75]. Furthermore, recently it has been realised that many real-world complex systems having different types of interactions can be modeled using multiplex or multilayer networks framework [76][77][78]. Motivated from the success of this new framework in predicting various behaviours of interacting units, spectra of adjacency and Laplacian matrices have also been investigated, particularly in the context of understanding occurrence of extreme events [79] and diffusion processes [80] on multilayer networks.…”

Section: Conclusion and Other Directionsmentioning

confidence: 99%

Spectral properties of complex networks

Sarkar

Jalan

2018

Chaos: An Interdisciplinary Journal of Nonlinear Science

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This review presents an account of the major works done on spectra of adjacency matrices drawn on networks and the basic understanding attained so far. We have divided the review under three sections: (a) extremal eigenvalues, (b) bulk part of the spectrum and (c) degenerate eigenvalues, based on the intrinsic properties of eigenvalues and the phenomena they capture. We have reviewed the works done for spectra of various popular model networks, such as the Erdős-Rényi random networks, scale-free networks, 1-d lattice, small-world networks, and various different real-world networks. Additionally, potential applications of spectral properties for natural processes have been reviewed.Various natural and man-made systems have been modeled under the network theory framework. Different network models with distinct design principles have been proposed to better understand these real-world networks. The eigenvalue spectrum of these networks not only contain information about structural characteristics of underlying networks but also provide insight to dynamical behaviour and stability of corresponding complex systems. Depending on the structural characteristics of underlying model networks, the spectra of these networks exhibit specific features. All these ascertain that the spectra of networks can be used as a practical tool for classifying and understanding different real-world systems represented as networks. In this review, we first discussed the features which the different regions of spectra furnish, in case of the model networks. Further, we went on to discuss spectral characteristics of real-world networks, with particular emphasis on their extent of similarities and differences with the spectra of model networks.has elucidated the importance of interactions which has led to fundamental understanding of emergent phenomena in various complex systems and processes, for instance, cellular signalling, disease spread, scientific collaboration, transportation, WWW, power grid and so on [1,2]. Many of these networks appear to share certain nontrivial, similar patterns in connections between their elements. An understanding to the origins of these patterns and identifying and characterizing new ones is one of the main driving forces for research in complex networks. Apart from various investigations which focus on direct measurements of the structural properties of networks, there have been studies demonstrating that properties of networks or graphs could be well characterized by the spectrum of associated adjacency matrix [3]. The spectrum of a network is the set of eigenvalues of its adjacency matrix (A ij ) and is denoted as λ i , where i = 1, 2, . . . , N such that λ 1 > λ 2 ≥ λ 3 ≥ . . . ≥ λ N . For an undirected network, the adjacency matrix is symmetric and consequently has real eigenvalues. For a directed network, the adjacency matrix is asymmetric and has complex eigenvalues. Further, there can be networks having weighted connections, negative couplings, etc. Note that here we have restricted ourselves to symmet...

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“…Multiplex networks ( Fig. 1) are defined as a collection of two or more layers which share the same nodes but have different connectivities in each layer [12]. Thus they describe networks which possess more than one type of interaction within the same elements, such as transport networks (with different means of travel as different layers), biochemical networks (with different signaling channels representing different layers), etc.…”

Section: Introductionmentioning

confidence: 99%

Non-identical multiplexing promotes chimera states

Ghosh

Zakharova

Jalan

2018

Chaos, Solitons & Fractals

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We present the emergence of chimeras, a state referring to coexistence of partly coherent, partly incoherent dynamics in networks of identical oscillators, in a multiplex network consisting of two non-identical layers which are interconnected. We demonstrate that the parameter range displaying the chimera state in the homogeneous first layer of the multiplex networks can be tuned by changing the link density or connection architecture of the same nodes in the second layer. We focus on the impact of the interconnected second layer on the enlargement or shrinking of the coupling regime for which chimeras are displayed in the homogeneous first layer. We find that a denser homogeneous second layer promotes chimera in a sparse first layer, where chimeras do not occur in isolation. Furthermore, while a dense connection density is required for the second layer if it is homogeneous, this is not true if the second layer is inhomogeneous. We demonstrate that a sparse inhomogeneous second layer which is common in real-world complex systems, can promote chimera states in a sparse homogeneous first layer.

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Multilayer network decoding versatility and trust

Cited by 28 publications

References 50 publications

Longitudinal network theory approaches identify crucial factors affecting sporulation efficiency in yeast

Longitudinal network theory approaches identify crucial factors affecting sporulation efficiency in yeast

Spectral properties of complex networks

Non-identical multiplexing promotes chimera states

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