Complex Networks In Order Research Articles

Inferring synchronizability of networked heterogeneous oscillators with machine learning.

In the study of network synchronization, an outstanding question of both theoretical and practical significance is how to allocate a given set of heterogeneous oscillators on a complex network in order to improve the synchronization performance. Whereas methods have been proposed to address this question in the literature, the methods are all based on accurate models describing the system dynamics, which, however, are normally unavailable in realistic situations. Here, we show that this question can be addressed by the model-free technique of a feed-forward neural network (FNN) in machine learning. Specifically, we measure the synchronization performance of a number of allocation schemes and use the measured data to train a machine. It is found that the trained machine is able to not only infer the synchronization performance of any new allocation scheme, but also find from a huge amount of candidates the optimal allocation scheme for synchronization.

A reduced model for complex network analysis of public transportation systems

Public transportation networks (PTNs) are represented as complex networks in order to analyze their robustness regarding node and link failures, to classify them into different theoretical network models, and to identify the characteristics of the underlying network. Usually, PTNs have a large amount of 1- and 2- degree nodes that blur the analysis and their characterization as complex networks. Subway and train-based transport networks present long single lines that connect central stations to far destinations differently from airport networks that usually have few large airports (hubs) connecting a significant number of small airports. By focusing on relevant network nodes and links and allowing comparisons between PTNs of different transportation modes, this paper proposes the Reduced Model as a simple method of network reduction that preserves the network skeleton (backbone structure) by properly removing 2-degree nodes of weighted and unweighted network representations. Different from other proposed methods, its simple formulation leads to mathematical expressions that show how the reduced model affects fundamental network metrics (degree, path length, and clustering coefficient distributions). The Reduced model is applied to four large real-world PTNs: (i) two Brazilian cities with bus-based transport; (ii) the Seoul metro network; (iii) a worldwide airport network. The results reveal a hub-based hierarchical structure when a large number of intermediary stops are present and small-world properties that emphasizes hub–hub connections after applying the Reduced model. Therefore, the reduced model emphasizes characteristics of the networks that could be difficult to identify without reduction.

A Survey on One-shot Neural Architecture Search

Complex deep neural network architecture such as AlexNet has great success in image classification, natural language processing and other applications. The choice of network architecture has proven to be critical. And researchers begin to design more and more complex networks in order to obtain better performance. However, designing complex networks manually often consumes a lot of computing resources and time, so the automated Neural Architecture Search(NAS) has attracted more and more attention in recent years. Currently, NAS have shown great potential in designing new architectures with high performance and high efficiency. Thus, first of all, this paper briefly introduces the development context and different directions of NAS. But compared to other NAS surveys, this paper focuses on One-shot-NAS methods. Among different methods of NAS, One-shot-NAS can achieve better performance in less time. Through comprehensive comparison and analysis, we apply a novel categorization on One-shot-NAS methods that are based on DAG, Hypernetwork, and network transformation respectively.

Analysis of Coauthorship Network in Political Science using Centrality Measures

In recent era, networks of data are growing massively and forming a shape of complex structure. Data scientists try to analyze different complex networks and utilize these networks to understand the complex structure of a network in a meaningful way. There is a need to detect and identify such a complex network in order to know how these networks provide communication means while using the complex structure. Social network analysis provides methods to explore and analyze such complex networks using graph theories, network properties and community detection algorithms. In this paper, an analysis of co-authorship network of Public Relation and Public Administration subjects of Microsoft Academic Graph (MAG) is presented, using common centrality measures. The authors belong to different research and academic institutes present all over the world. Cohesive groups of authors have been identified and ranked on the basis of centrality measures, such as betweenness, degree, page rank and closeness. Experimental results show the discovery of authors who are good in specific domain, have a strong field knowledge and maintain collaboration among their peers in the field of Public Relations and Public Administration.

Network Anatomy Controlling Abrupt-like Percolation Transition

We virtually dissect complex networks in order to understand their internal structure, just as doctors do with the bodies of animals. Our novel method classifies network links into four categories: bone, fat, cartilage, and muscle, based on network connectivity. We derive an efficient percolation strategy from this new viewpoint of network anatomy, which enables abrupt-like percolation transition through removal of a small amount of cartilage links, which play a crucial role in network connectivity. Furthermore, we find nontrivial scaling laws in the relationships between four types of links in each cluster and evaluate power exponents, which characterize network structures as seen in the real large-scale network of trading business firms and in the Erdős-Rényi network. Finally, we observe changes in the transition point for random bond percolation process, demonstrating that the addition of muscle links enhances network robustness, while fat links are irrelevant. These findings aid in controlling the percolation transition for an arbitrary network.

Nonlinear characterization of gas liquid two-phase flow in complex networks

A new method is proposed to study nonlinear dynamic of gas liquid two-phase flow through a vertical upward pipe with the aim to make clear the two-phase flow behavior, from the complex networks perspective. The experimental test section is a transparent plexiglass pipe with inner diameter is 40mm and the length is 1.5m. Five kinds of flow pattern including bubble flow, bubble–slug flow, slug flow, slug–churn flow and churn flow are measured, and then constructed 178 corresponding flow pattern complex networks in order to reveal the dynamical characteristics of two-phase flow. It is found that different flow pattern complex network present different community structure, when the correspondence between the threshold and the mean of similarity matrix meet specific proportion. The results show that the power-law exponent of degree distribution and network information entropy of flow pattern complex network is sensitive to the flow pattern transition, which can be employed to characterize the nonlinear dynamics of two-phase flow. It is demonstrated that complex network approach can be an effective tool to study nonlinear dynamics of air water two-phase flow.

Complex networks, dynamics and neural information processing in the brain

Event Abstract Back to Event Complex networks, dynamics and neural information processing in the brain Claudio Mirasso1* and Gordon Pila2 1 IFISC, CSIC-UIB, Spain 2 Institute of Cognitive Science, University of Osnabrueck, Germany Nowadays technologies like FRMI, MEG, EEG, optical imaging, and high- density recordings of spiking activity, as well as tools to investigate anatomical details, describe properties of the neuronal dynamics increasingly precisely. These techniques reveal that information-processing capabilities of the human brain rely on its ability to operate on multiple temporal and spatial scales in a coordinated way. Temporal scales range from very fast processes (sub milliseconds) to years. Spatially, neural systems range from very local elements like dendritic compartments and synapses, to columnar structures and whole brain regions. Thought much is known about the processing on individual scales, i.e. single neurons or local networks, very little is known about large scale networks and the neuronal computations occurring at different spatial and temporal scales. For instance, the neural mechanisms that integrate distributed activity arising from cognition, memory, etc., to create a unique concept from an event, remain unclear. A better understanding of how this process occurs represents one of the current frontiers in neuroscience. In this symposium we will discuss new approaches to study and characterize complex networks in order to gain insight into those neuronal processes and information processing that span over different temporal and spatial scales. Specifically, the dynamics occurring in large complex neuronal interacting networks will be a major topic, as well as the synchronization properties. Beyond these aspects, the symposium will discuss models that help us to better understand and exploit this complex dynamics for information processing. Keywords: EEG, fMRI, MEG, Neuronal Processes Conference: XI International Conference on Cognitive Neuroscience (ICON XI), Palma, Mallorca, Spain, 25 Sep - 29 Sep, 2011. Presentation Type: Introduction Topic: Abstracts Citation: Mirasso C and Pila G (2011). Complex networks, dynamics and neural information processing in the brain. Conference Abstract: XI International Conference on Cognitive Neuroscience (ICON XI). doi: 10.3389/conf.fnhum.2011.207.00530 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 11 Nov 2011; Published Online: 28 Nov 2011. * Correspondence: Prof. Claudio Mirasso, IFISC, CSIC-UIB, Palma, Mallorca, Spain, claudio@ifisc.uib-csic.es Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Claudio Mirasso Gordon Pila Google Claudio Mirasso Gordon Pila Google Scholar Claudio Mirasso Gordon Pila PubMed Claudio Mirasso Gordon Pila Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.