Network Science Based Quantification of Resilience Demonstrated on the Indian Railways Network
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Emerging Topics in Internet Technology: a Complex Networks Approach
1 Emerging Topics in Internet Technology: A Complex Networks Approach Bisma S. Khan 1, Muaz A. Niazi *,2 COMSATS Institute of Information Technology, Islamabad, Pakistan Abstract —Communication networks, in general, and internet technology, in particular, is a fast-evolving area of research. While it is important to keep track of emerging trends in this domain, it is such a fast-growing area that it can be very difficult to keep track of literature. The problem is compounded by the fast-growing number of citation databases. While other databases are gradually indexing a large set of reliable content, currently the Web of Science represents one of the most highly valued databases. Research indexed in this database is known to highlight key advancements in any domain. In this paper, we present a Complex Network-based analytical approach to analyze recent data from the Web of Science in communication networks. Taking bibliographic records from the recent period of 2014 to 2017 , we model and analyze complex scientometric networks. Using bibliometric coupling applied over complex citation data we present answers to co-citation patterns of documents, co-occurrence patterns of terms, as well as the most influential articles, among others, We also present key pivot points and intellectual turning points. Complex network analysis of the data demonstrates a considerably high level of interest in two key clusters labeled descriptively as “social networks” and “computer networks”. In addition, key themes in highly cited literature were clearly identified as “communication networks,” “social networks,” and “complex networks”. Index Terms —CiteSpace, Communication Networks, Network Science, Complex Networks, Visualization, Data science. -
Breakdown of Interdependent Directed Networks Xueming Liu, ∗ † H
i i \"SI Appendix"" | 2015/12/23 | 9:13 | page 1 | #1 i i Supporting Information: Breakdown of interdependent directed networks Xueming Liu, ∗ y H. Eugene Stanley y and Jianxi Gao z ∗Key Laboratory of Image Information Processing and Intelligent Control, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China,yCenter for Polymer Studies and Department of Physics, Boston University, Boston, MA 02215, and zCenter for Complex Network Research and Department of Physics, Northeastern University, Boston, MA 02115 Submitted to Proceedings of the National Academy of Sciences of the United States of America I. Notions related to interdependent networks Multiplex networks. The agents (nodes) participate in ev- Both natural and engineered complex systems are not isolated ery layer of the network simultaneously. The connections but interdependent and interconnected. Such diverse infras- among these agents in different layers represent different tructures as water supply systems, transportation networks, relationships [14, 15, 28, 29, 13, 30, 31]. For example, a fuel delivery systems, and power stations are coupled together user of online social networks can subscribe to two or more [1]. To study the interdependence between networks, Buldyrev networks and build social relationships with other users on et al. [2] developed an analytic framework based on the gener- a range of social platforms (e.g., LinkedIn for a network of ating function formalism [3, 4] and discovered that the interde- professional contacts or Facebook for a network of friends) pendence between networks sharply increases system vulner- [11, 14]. ability, because node failures in one network can lead to the Temporal networks. -
Introduction to Network Science & Visualisation
IFC – Bank Indonesia International Workshop and Seminar on “Big Data for Central Bank Policies / Building Pathways for Policy Making with Big Data” Bali, Indonesia, 23-26 July 2018 Introduction to network science & visualisation1 Kimmo Soramäki, Financial Network Analytics 1 This presentation was prepared for the meeting. The views expressed are those of the author and do not necessarily reflect the views of the BIS, the IFC or the central banks and other institutions represented at the meeting. FNA FNA Introduction to Network Science & Visualization I Dr. Kimmo Soramäki Founder & CEO, FNA www.fna.fi Agenda Network Science ● Introduction ● Key concepts Exposure Networks ● OTC Derivatives ● CCP Interconnectedness Correlation Networks ● Housing Bubble and Crisis ● US Presidential Election Network Science and Graphs Analytics Is already powering the best known AI applications Knowledge Social Product Economic Knowledge Payment Graph Graph Graph Graph Graph Graph Network Science and Graphs Analytics “Goldman Sachs takes a DIY approach to graph analytics” For enhanced compliance and fraud detection (www.TechTarget.com, Mar 2015). “PayPal relies on graph techniques to perform sophisticated fraud detection” Saving them more than $700 million and enabling them to perform predictive fraud analysis, according to the IDC (www.globalbankingandfinance.com, Jan 2016) "Network diagnostics .. may displace atomised metrics such as VaR” Regulators are increasing using network science for financial stability analysis. (Andy Haldane, Bank of England Executive -
What Metrics Really Mean, a Question of Causality and Construction in Leveraging Social Media Audiences Into Business Results: Cases from the UK Film Industry
. Volume 10, Issue 2 November 2013 What metrics really mean, a question of causality and construction in leveraging social media audiences into business results: Cases from the UK film industry Michael Franklin, University of St Andrews, UK Summary: Digital distribution and sustained audience engagement potentially offer filmmakers new business models for responding to decreasing revenues from DVD and TV rights. Key to campaigns that aim at enrolling audiences, mitigating demand uncertainty and improving revenues, is the management of digital media metrics. This process crucially involves the interpretation of figures where a causal gap exists between some digital activity and a market transaction. This paper charts the conceptual framing methods and calculations of value necessary to manipulating and utilising the audience in a new way. The invisible aspects and mediating role of contemporary creative industries audiences is presented through empirical case study evidence from two UK feature films. Practitioners’ understanding of digital engagement metrics is shown to be a social construction involving the agency of material artefacts as powerful elements of networks that include both market entities and audiences. Keywords: Film Business; Social Media; Market Device; Valuation; Digital Engagement Introduction The affordances of digital technology for audience engagement and more direct, or dis- intermediated distribution, prompt a reappraisal of the role of audiences in the film industry. Through digital tools, most significantly through social media, audiences are made material. Audiences are interacted with, engaged, marketed to, and financially exploited often visible all their social network connections, but they are also aggregated, manipulated and exert an agency that is much less visible and plays out off-screen. -
A Network-Of-Networks Percolation Analysis of Cascading Failures in Spatially Co-Located Road-Sewer Infrastructure Networks
Physica A 538 (2020) 122971 Contents lists available at ScienceDirect Physica A journal homepage: www.elsevier.com/locate/physa A network-of-networks percolation analysis of cascading failures in spatially co-located road-sewer infrastructure networks ∗ ∗ Shangjia Dong a, , Haizhong Wang a, , Alireza Mostafizi a, Xuan Song b a School of Civil and Construction Engineering, Oregon State University, Corvallis, OR 97331, United States of America b Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China article info a b s t r a c t Article history: This paper presents a network-of-networks analysis framework of interdependent crit- Received 26 September 2018 ical infrastructure systems, with a focus on the co-located road-sewer network. The Received in revised form 28 May 2019 constructed interdependency considers two types of node dynamics: co-located and Available online 27 September 2019 multiple-to-one dependency, with different robustness metrics based on their function Keywords: logic. The objectives of this paper are twofold: (1) to characterize the impact of the Co-located road-sewer network interdependency on networks' robustness performance, and (2) to unveil the critical Network-of-networks percolation transition threshold of the interdependent road-sewer network. The results Percolation modeling show that (1) road and sewer networks are mutually interdependent and are vulnerable Infrastructure interdependency to the cascading failures initiated by sewer system disruption; (2) the network robust- Cascading failure ness decreases as the number of initial failure sources increases in the localized failure scenarios, but the rate declines as the number of failures increase; and (3) the sewer network contains two types of links: zero exposure and severe exposure to liquefaction, and therefore, it leads to a two-phase percolation transition subject to the probabilistic liquefaction-induced failures. -
Multilayer Networks
Journal of Complex Networks (2014) 2, 203–271 doi:10.1093/comnet/cnu016 Advance Access publication on 14 July 2014 Multilayer networks Mikko Kivelä Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK Alex Arenas Departament d’Enginyeria Informática i Matemátiques, Universitat Rovira I Virgili, 43007 Tarragona, Spain Marc Barthelemy Downloaded from Institut de Physique Théorique, CEA, CNRS-URA 2306, F-91191, Gif-sur-Yvette, France and Centre d’Analyse et de Mathématiques Sociales, EHESS, 190-198 avenue de France, 75244 Paris, France James P. Gleeson MACSI, Department of Mathematics & Statistics, University of Limerick, Limerick, Ireland http://comnet.oxfordjournals.org/ Yamir Moreno Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza 50018, Spain and Department of Theoretical Physics, University of Zaragoza, Zaragoza 50009, Spain and Mason A. Porter† Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, by guest on August 21, 2014 Oxford OX2 6GG, UK and CABDyN Complexity Centre, University of Oxford, Oxford OX1 1HP, UK †Corresponding author. Email: [email protected] Edited by: Ernesto Estrada [Received on 16 October 2013; accepted on 23 April 2014] In most natural and engineered systems, a set of entities interact with each other in complicated patterns that can encompass multiple types of relationships, change in time and include other types of complications. Such systems include multiple subsystems and layers of connectivity, and it is important to take such ‘multilayer’ features into account to try to improve our understanding of complex systems. Consequently, it is necessary to generalize ‘traditional’ network theory by developing (and validating) a framework and associated tools to study multilayer systems in a comprehensive fashion. -
Evolving Networks and Social Network Analysis Methods And
DOI: 10.5772/intechopen.79041 ProvisionalChapter chapter 7 Evolving Networks andand SocialSocial NetworkNetwork AnalysisAnalysis Methods and Techniques Mário Cordeiro, Rui P. Sarmento,Sarmento, PavelPavel BrazdilBrazdil andand João Gama Additional information isis available atat thethe endend ofof thethe chapterchapter http://dx.doi.org/10.5772/intechopen.79041 Abstract Evolving networks by definition are networks that change as a function of time. They are a natural extension of network science since almost all real-world networks evolve over time, either by adding or by removing nodes or links over time: elementary actor-level network measures like network centrality change as a function of time, popularity and influence of individuals grow or fade depending on processes, and events occur in net- works during time intervals. Other problems such as network-level statistics computation, link prediction, community detection, and visualization gain additional research impor- tance when applied to dynamic online social networks (OSNs). Due to their temporal dimension, rapid growth of users, velocity of changes in networks, and amount of data that these OSNs generate, effective and efficient methods and techniques for small static networks are now required to scale and deal with the temporal dimension in case of streaming settings. This chapter reviews the state of the art in selected aspects of evolving social networks presenting open research challenges related to OSNs. The challenges suggest that significant further research is required in evolving social networks, i.e., existent methods, techniques, and algorithms must be rethought and designed toward incremental and dynamic versions that allow the efficient analysis of evolving networks. Keywords: evolving networks, social network analysis 1. -
Network Science, Homophily and Who Reviews Who in the Linux Kernel? Working Paper[+] Open-Access at ECIS2020-Arxiv.Pdf
Network Science, Homophily and Who Reviews Who in the Linux Kernel? Working paper[P] Open-access at http://users.abo.fi/jteixeir/pub/linuxsna/ ECIS2020-arxiv.pdf José Apolinário Teixeira Åbo Akademi University Finland # jteixeira@abo. Ville Leppänen University of Turku Finland Sami Hyrynsalmi arXiv:2106.09329v1 [cs.SE] 17 Jun 2021 LUT University Finland [P]As presented at 2020 European Conference on Information Systems (ECIS 2020), held Online, June 15-17, 2020. The ocial conference proceedings are available at the AIS eLibrary (https://aisel.aisnet.org/ecis2020_rp/). Page ii of 24 ? Copyright notice ? The copyright is held by the authors. The same article is available at the AIS Electronic Library (AISeL) with permission from the authors (see https://aisel.aisnet.org/ ecis2020_rp/). The Association for Information Systems (AIS) can publish and repro- duce this article as part of the Proceedings of the European Conference on Information Systems (ECIS 2020). ? Archiving information ? The article was self-archived by the rst author at its own personal website http:// users.abo.fi/jteixeir/pub/linuxsna/ECIS2020-arxiv.pdf dur- ing June 2021 after the work was presented at the 28th European Conference on Informa- tion Systems (ECIS 2020). Page iii of 24 ð Funding and Acknowledgements ð The rst author’s eorts were partially nanced by Liikesivistysrahasto - the Finnish Foundation for Economic Education, the Academy of Finland via the DiWIL project (see http://abo./diwil) project. A research companion website at http://users.abo./jteixeir/ECIS2020cw supports the paper with additional methodological details, additional data visualizations (plots, tables, and networks), as well as high-resolution versions of the gures embedded in the paper. -
Network Science
This is a preprint of Katy Börner, Soma Sanyal and Alessandro Vespignani (2007) Network Science. In Blaise Cronin (Ed) Annual Review of Information Science & Technology, Volume 41. Medford, NJ: Information Today, Inc./American Society for Information Science and Technology, chapter 12, pp. 537-607. Network Science Katy Börner School of Library and Information Science, Indiana University, Bloomington, IN 47405, USA [email protected] Soma Sanyal School of Library and Information Science, Indiana University, Bloomington, IN 47405, USA [email protected] Alessandro Vespignani School of Informatics, Indiana University, Bloomington, IN 47406, USA [email protected] 1. Introduction.............................................................................................................................................2 2. Notions and Notations.............................................................................................................................4 2.1 Graphs and Subgraphs .........................................................................................................................5 2.2 Graph Connectivity..............................................................................................................................7 3. Network Sampling ..................................................................................................................................9 4. Network Measurements........................................................................................................................11 -
Multidimensional Network Analysis
Universita` degli Studi di Pisa Dipartimento di Informatica Dottorato di Ricerca in Informatica Ph.D. Thesis Multidimensional Network Analysis Michele Coscia Supervisor Supervisor Fosca Giannotti Dino Pedreschi May 9, 2012 Abstract This thesis is focused on the study of multidimensional networks. A multidimensional network is a network in which among the nodes there may be multiple different qualitative and quantitative relations. Traditionally, complex network analysis has focused on networks with only one kind of relation. Even with this constraint, monodimensional networks posed many analytic challenges, being representations of ubiquitous complex systems in nature. However, it is a matter of common experience that the constraint of considering only one single relation at a time limits the set of real world phenomena that can be represented with complex networks. When multiple different relations act at the same time, traditional complex network analysis cannot provide suitable an- alytic tools. To provide the suitable tools for this scenario is exactly the aim of this thesis: the creation and study of a Multidimensional Network Analysis, to extend the toolbox of complex network analysis and grasp the complexity of real world phenomena. The urgency and need for a multidimensional network analysis is here presented, along with an empirical proof of the ubiquity of this multifaceted reality in different complex networks, and some related works that in the last two years were proposed in this novel setting, yet to be systematically defined. Then, we tackle the foundations of the multidimensional setting at different levels, both by looking at the basic exten- sions of the known model and by developing novel algorithms and frameworks for well-understood and useful problems, such as community discovery (our main case study), temporal analysis, link prediction and more. -
Cascading Failures in Interdependent Networks with Multiple Supply-Demand Links and Functionality Thresholds Supplementary Information M
Cascading Failures in Interdependent Networks with Multiple Supply-Demand Links and Functionality Thresholds Supplementary Information M. A. Di Muro1,*, L. D. Valdez2,3, H. H. Aragao˜ Regoˆ 4, S. V. Buldyrev5, H.E. Stanley6, and L. A. Braunstein1,6 1Instituto de Investigaciones F´ısicas de Mar del Plata (IFIMAR)-Departamento de F´ısica, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata-CONICET, Funes 3350, (7600) Mar del Plata, Argentina. 2Instituto de F´ısica Enrique Gaviola, CONICET, Ciudad Universitaria, 5000 Cordoba,´ Argentina. 3Facultad de Matematica,´ Astronom´ıa, F´ısica y Computacion,´ Universidad Nacional de Cordoba,´ Cordoba,´ Argentina 4Departamento de F´ısica, Instituto Federal de Educac¸ao,˜ Cienciaˆ e Tecnologia do Maranhao,˜ Sao˜ Lu´ıs, MA, 65030-005, Brazil 5Department of Physics, Yeshiva University, 500 West 185th Street, New York, New York 10033, USA 6Center for Polymer Studies, Boston University, Boston, Massachusetts 02215, USA *[email protected] Explicit form of the functionality rules Giant component The giant component in a network is the largest connected component. Most functioning networks are completely connected, but when they experience failure, finite components—little islands of nodes— become disconnected from the giant component. A common functionality rule states that nodes in these finite components have insufficient support to remain active. Thus in addition to the nodes rendered inactive by failure, the exacerbation factor renders inactive all nodes not connected to the giant component. If network X has a degree distribution PX (k) and a fraction 1 − yX of nodes is randomly removed, the X X X exacerbation factor gX is gX (yX ) = 1 − G0 [1 − yX (1 − f¥ )], where f¥ is the probability that the branches X X X do not expand to infinity, and it satisfies the recurrent equation f¥ = G1 [1 − yX (1 − f¥ )]. -
Complex Networks in Computer Vision
Complex networks in computer vision Pavel Voronin Shape to Network [Backes 2009], [Backes 2010a], [Backes 2010b] Weights + thresholds Feature vectors Features = characteris>cs of thresholded undirected binary networks: degree or joint degree (avg / min / max), avg path length, clustering coefficient, etc. Mul>scale Fractal Dimension Properes • Only uses distances => rotaon invariant • Weights normalized by max distance => scale invariant • Uses pixels not curve elements => robust to noise and outliers => applicable to skeletons, mul>ple contours Face recogni>on [Goncalves 2010], [Tang 2012a] Mul>ple binarizaon thresholds Texture to Network [Chalumeau 2008], [Backes 2010c], [Backes 2013] Thresholds + features Features: degree, hierarchical degree (avg / min / max) Invariance, robustness Graph structure analysis [Tang 2012b] Graph to Network Thresholds + descriptors Degree, joint degree, clustering-distance Saliency Saccade eye movements + different fixaon >me = saliency map => model them as walks in networks [Harel 2006], [Costa 2007], [Gopalakrishnan 2010], [Pal 2010], [Kim 2013] Network construc>on Nodes • pixels • segments • blocks Edges • local (neighbourhood) • global (most similar) • both Edge weights = (distance in feature space) / (distance in image space) Features: • relave intensity • entropy of local orientaons • compactness of local colour Random walks Eigenvector centrality == staonary distribu>on for Markov chain == expected >me a walker spends in the node Random walk with restart (RWR) If we have prior info on node importance,