scholarly journals From a single network to a network of networks

2014 ◽  
Vol 1 (3) ◽  
pp. 346-356 ◽  
Author(s):  
Jianxi Gao ◽  
Daqing Li ◽  
Shlomo Havlin
Keyword(s):  
Biological Networks ◽  
Network Science ◽  
Small World ◽  
Spatial Constraints ◽  
Scale Free ◽  
Gene Regulation Network ◽  
Interdependent Networks ◽  
Interconnected Networks ◽  
The Rich ◽  
Network Of Networks

Abstract Network science has attracted much attention in recent years due to its interdisciplinary applications. We witnessed the revolution of network science in 1998 and 1999 started with small-world and scale-free networks having now thousands of high-profile publications, and it seems that since 2010 studies of ‘network of networks’ (NON), sometimes called multilayer networks or multiplex, have attracted more and more attention. The analytic framework for NON yields a novel percolation law for n interdependent networks that shows that percolation theory of single networks studied extensively in physics and mathematics in the last 50 years is a specific limit of the rich and very different general case of n coupled networks. Since then, properties and dynamics of interdependent and interconnected networks have been studied extensively, and scientists are finding many interesting results and discovering many surprising phenomena. Because most natural and engineered systems are composed of multiple subsystems and layers of connectivity, it is important to consider these features in order to improve our understanding of such complex systems. Now the study of NON has become one of the important directions in network science. In this paper, we review recent studies on the new emerging area—NON. Due to the fast growth of this field, there are many definitions of different types of NON, such as interdependent networks, interconnected networks, multilayered networks, multiplex networks and many others. There exist many datasets that can be represented as NON, such as network of different transportation networks including flight networks, railway networks and road networks, network of ecological networks including species interacting networks and food webs, network of biological networks including gene regulation network, metabolic network and protein–protein interacting network, network of social networks and so on. Among them, many interdependent networks including critical infrastructures are embedded in space, introducing spatial constraints. Thus, we also review the progress on study of spatially embedded networks. As a result of spatial constraints, such interdependent networks exhibit extreme vulnerabilities compared with their non-embedded counterparts. Such studies help us to understand, realize and hopefully mitigate the increasing risk in NON.

Transcriptomics to Metabolomics

2018 ◽  
pp. 188-206 ◽  
Author(s):  
Ankush Bansal ◽  
Pulkit Anupam Srivastava
Keyword(s):  
Biological Networks ◽  
Metabolic Networks ◽  
Topological Analysis ◽  
In Silico Analysis ◽  
Small World ◽  
Recent Decade ◽  
Bipartite Networks ◽  
Metabolomics Data ◽  
Scale Free ◽  
Gene Regulatory

A lot of omics data is generated in a recent decade which flooded the internet with transcriptomic, genomics, proteomics and metabolomics data. A number of software, tools, and web-servers have developed to analyze the big data omics. This review integrates the various methods that have been employed over the years to interpret the gene regulatory and metabolic networks. It illustrates random networks, scale-free networks, small world network, bipartite networks and other topological analysis which fits in biological networks. Transcriptome to metabolome network is of interest because of key enzymes identification and regulatory hub genes prediction. It also provides an insight into the understanding of omics technologies, generation of data and impact of in-silico analysis on the scientific community.

Transcriptomics to Metabolomics

Biotechnology ◽  
2019 ◽  
pp. 361-379
Author(s):  
Ankush Bansal ◽  
Pulkit Anupam Srivastava
Keyword(s):  
Biological Networks ◽  
Metabolic Networks ◽  
Topological Analysis ◽  
In Silico Analysis ◽  
Small World ◽  
Recent Decade ◽  
Bipartite Networks ◽  
Metabolomics Data ◽  
Scale Free ◽  
Gene Regulatory

A lot of omics data is generated in a recent decade which flooded the internet with transcriptomic, genomics, proteomics and metabolomics data. A number of software, tools, and web-servers have developed to analyze the big data omics. This review integrates the various methods that have been employed over the years to interpret the gene regulatory and metabolic networks. It illustrates random networks, scale-free networks, small world network, bipartite networks and other topological analysis which fits in biological networks. Transcriptome to metabolome network is of interest because of key enzymes identification and regulatory hub genes prediction. It also provides an insight into the understanding of omics technologies, generation of data and impact of in-silico analysis on the scientific community.

scholarly journals Scaling in Colloidal and Biological Networks

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 622 ◽  
Author(s):  
Michael Nosonovsky ◽  
Prosun Roy
Keyword(s):  
Neural Networks ◽  
Biological Networks ◽  
Surface Engineering ◽  
Brain Size ◽  
Small World ◽  
Colloidal Systems ◽  
Vascular Networks ◽  
Technology Scaling ◽  
Scale Free ◽  
Self Organized Criticality

Scaling and dimensional analysis is applied to networks that describe various physical systems. Some of these networks possess fractal, scale-free, and small-world properties. The amount of information contained in a network is found by calculating its Shannon entropy. First, we consider networks arising from granular and colloidal systems (small colloidal and droplet clusters) due to pairwise interaction between the particles. Many networks found in colloidal science possess self-organizing properties due to the effect of percolation and/or self-organized criticality. Then, we discuss the allometric laws in branching vascular networks, artificial neural networks, cortical neural networks, as well as immune networks, which serve as a source of inspiration for both surface engineering and information technology. Scaling relationships in complex networks of neurons, which are organized in the neocortex in a hierarchical manner, suggest that the characteristic time constant is independent of brain size when interspecies comparison is conducted. The information content, scaling, dimensional, and topological properties of these networks are discussed.

The model of microblog message diffusion based on complex social network

2014 ◽  
Vol 28 (20) ◽  
pp. 1450136 ◽  
Author(s):  
Wei Zhang ◽  
Shu-Ying Bai ◽  
Rui Jin
Keyword(s):  
Small World ◽  
Typical Feature ◽  
Negative Influence ◽  
Typical Case ◽  
Sina Weibo ◽  
Scale Free ◽  
Topological Features ◽  
Speed Up ◽  
The Rich

Microblog is a micromessage communication network in which users are the nodes and the followship between users are the edges. Sina Weibo is a typical case of these microblog service websites. As the enormous scale of nodes and complex links in the network, we choose a sample network crawled in Sina Weibo as the base of empirical analysis. The study starts with the analysis of its topological features, and brings in epidemiological SEIR model to explore the mode of message spreading throughout the microblog network. It is found that the network is obvious small-world and scale-free, which made it succeed in transferring messages and failed in resisting negative influence. In addition, the paper focuses on the rich nodes as they constitute a typical feature of Sina Weibo. It is also found that whether the message starts with a rich node will not account for its final coverage. Actually, the rich nodes always play the role of pivotal intermediaries who speed up the spreading and make the message known by much more people.

Using network science and psycholinguistic megastudies to examine the dimensions of phonological similarity

2020 ◽  
Author(s):  
Nichol Castro ◽  
Michael Vitevitch
Keyword(s):  
Network Science ◽  
Small World ◽  
Phonological Similarity ◽  
Neighborhood Density ◽  
Theoretic Approach ◽  
Scale Free ◽  
Advantages And Disadvantages ◽  
Behavioral Evidence ◽  
The One ◽  
Information Theoretic Approach

We used the tools of network science to examine different dimensions of phonological similarity. Data from a phonological associate task and from an identification of words in noise task were used to create two separate networks. The resulting networks were compared to each other and to a network formed by a computational metric (i.e., one-phoneme metric) widely-used to assess phonological similarity using an information-theoretic approach and a variety of network measures (e.g., small-world structure, scale-free structure, mixing by degree, location of nodes in the network, and community structure). While we found that a network formed by the one-phoneme metric was structurally less similar to the network formed from the phonological associate task and to the network formed from the identification of words in noise task than the latter two were to each other, there were also several common network structure features between the one-phoneme metric network and the phonological association network. We then compared the influence of degree (equivalent to neighborhood density) from each of the networks on behavioral data, namely reaction time on visual and auditory lexical decision tasks, obtained from two psycholinguistic megastudies to provide behavioral evidence for differences in network structures. We found that the effect of degree differed across network types and tasks. We discuss the advantages and disadvantages of each approach to determining phonological similarity, the implications of using each approach, and a possible direction forward for language research through the use of multiplex networks.

scholarly journals Large Scale-Free Network Organization is Likely Key for Biofilm Phase Transition

2019 ◽  
Author(s):  
Kumar Selvarajoo
Keyword(s):  
Coupling Strength ◽  
Biological Networks ◽  
Large Scale ◽  
Network Connectivity ◽  
Small World ◽  
Current Data ◽  
Kuramoto Model ◽  
Small Scale ◽  
Scale Free ◽  
Basic Model

AbstractNon-linear Kuramoto model has been used to study synchronized or sync behavior in numerous fields, however, its application in biology is scare. Here, I introduce the basic model and provide examples where large scale small-world or scale-free networks are crucial for spontaneous sync even for low coupling strength. This information was next checked for relevance in living systems where it is now well-known that biological networks are scale-free. Our recent transcriptome-wide data analysis of Saccharomyces cerevisiae biofilm showed that low to middle expressed genes are key for scale invariance in biology. Together, the current data indicate that biological network connectivity structure with low coupling strength, or expression levels, is sufficient for sync behavior. For biofilm regulation, it may, therefore, be necessary to investigate large scale low expression genes rather than small scale high expression genes.

Networks

2018 ◽  
Author(s):  
Stefan Thurner ◽  
Rudolf Hanel ◽  
Peter Klimekl
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Phase Diagrams ◽  
Community Detection ◽  
Network Science ◽  
Small World ◽  
Small World Networks ◽  
Multilayer Networks ◽  
Basic Vocabulary

Understanding the interactions between the components of a system is key to understanding it. In complex systems, interactions are usually not uniform, not isotropic and not homogeneous: each interaction can be specific between elements.Networks are a tool for keeping track of who is interacting with whom, at what strength, when, and in what way. Networks are essential for understanding of the co-evolution and phase diagrams of complex systems. Here we provide a self-contained introduction to the field of network science. We introduce ways of representing and handle networks mathematically and introduce the basic vocabulary and definitions. The notions of random- and complex networks are reviewed as well as the notions of small world networks, simple preferentially grown networks, community detection, and generalized multilayer networks.

scholarly journals Improvement on Load-Induced Cascading Failure in Asymmetrical Interdependent Networks: Modeling and Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Haiyan Han ◽  
Rennong Yang
Keyword(s):  
Load Capacity ◽  
Small World ◽  
Cascading Failure ◽  
Modeling And Analysis ◽  
Interdependent Networks ◽  
Interdependent Network ◽  
Capacity Model ◽  
Weight Structure ◽  
Edge Failure ◽  
Asymmetrical Model

Many real-world systems can be depicted as interdependent networks and they usually show an obvious property of asymmetry. Furthermore, node or edge failure can trigger load redistribution which leads to a cascade of failure in the whole network. In order to deeply investigate the load-induced cascading failure, firstly, an asymmetrical model of interdependent network consisting of a hierarchical weighted network and a WS small-world network is constructed. Secondly, an improved “load-capacity” model is applied for node failure and edge failure, respectively, followed by a series of simulations of cascading failure over networks in both interdependent and isolated statuses. The simulation results prove that the robustness in isolated network changes more promptly than that in the interdependent one. Network robustness is positively related to “capacity,” but negatively related to “load.” The hierarchical weight structure in the subnetwork leads to a “plateau” phenomenon in the progress of cascading failure.

Top Museums on Instagram

2019 ◽  
Vol 3 (2) ◽  
pp. 18-42 ◽  
Author(s):  
Vasiliki G. Vrana ◽  
Dimitrios A. Kydros ◽  
Evangelos C. Kehris ◽  
Anastasios-Ioannis T. Theocharidis ◽  
George I. Kavavasilis
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Small World ◽  
Marketing Strategies ◽  
The Other ◽  
Small World Network ◽  
Scale Free ◽  
Centrality Metrics ◽  
Photo Sharing ◽  
Visualization Algorithms

Pictures speak louder than words. In this fast-moving world where people hardly have time to read anything, photo-sharing sites become more and more popular. Instagram is being used by millions of people and has created a “sharing ecosystem” that also encourages curation, expression, and produces feedback. Museums are moving quickly to integrate Instagram into their marketing strategies, provide information, engage with audience and connect to other museums Instagram accounts. Taking into consideration that people may not see museum accounts in the same way that the other museum accounts do, the article first describes accounts' performance of the top, most visited museums worldwide and next investigates their interconnection. The analysis uses techniques from social network analysis, including visualization algorithms and calculations of well-established metrics. The research reveals the most important modes of the network by calculating the appropriate centrality metrics and shows that the network formed by the museum Instagram accounts is a scale–free small world network.

STABILITY OF TWO TYPICAL COMPLEX DYNAMICAL NETWORKS

2008 ◽  
Vol 22 (05) ◽  
pp. 553-560 ◽  
Author(s):  
WU-JIE YUAN ◽  
XIAO-SHU LUO ◽  
PIN-QUN JIANG ◽  
BING-HONG WANG ◽  
JIN-QING FANG
Keyword(s):  
Numerical Simulation ◽  
Dissipative System ◽  
Small World ◽  
Complex Dynamical Networks ◽  
Dynamical Networks ◽  
Scale Free ◽  
Scale Free Networks ◽  
General Complex ◽  
The Stability ◽  
Theoretical Results

When being constructed, complex dynamical networks can lose stability in the sense of Lyapunov (i. s. L.) due to positive feedback. Thus, there is much important worthiness in the theory and applications of complex dynamical networks to study the stability. In this paper, according to dissipative system criteria, we give the stability condition in general complex dynamical networks, especially, in NW small-world and BA scale-free networks. The results of theoretical analysis and numerical simulation show that the stability i. s. L. depends on the maximal connectivity of the network. Finally, we show a numerical example to verify our theoretical results.

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