DOCSLIB.ORG

Multidimensional Analysis of Complex Networks

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Multidimensional Analysis of Complex Networks Alma Mater Studiorum Universita’ di Bologna Computer Science Department Ph.D. Thesis Multidimensional analysis of complex networks Possamai Lino Ph.D. Supervisors: Prof. Massimo Marchiori Prof. Alessandro Sperduti Ph.D. Coordinator: Prof. Maurizio Gabbrielli DOTTORATO DI RICERCA IN INFORMATICA - Ciclo XXV Settore Concorsuale di afferenza: 01/B1 Settore Scientifico disciplinare: INF/01 Esame Finale Anno 2013 Ai miei genitori. Abstract The analysis of Complex Networks turn out to be a very promising field of research, testified by many research projects and works that span different fields. Until recently, those analysis have been usually focused on deeply characterize a single aspect of the system, therefore a study that considers many informative axes along with a network evolve is lacking. In this Thesis, we propose a new multidimensional analysis that is able to inspect networks in the two most important dimensions of a system, namely space and time. In order to achieve this goal, we studied them singularly and investigated how the variation of the constituting parameters drives changes to the network behavior as a whole. By focusing on space dimension, we were able to characterize spatial alteration in terms of abstraction levels. We propose a novel algorithm that, by applying a fuzziness function, can reconstruct networks under different level of details. We call this analysis telescopic as it recalls the magnification and reduction process of the lens. Through this line of research we have successfully verified that statistical indicators, that are frequently used in many complex networks researches, depends strongly on the granularity (i.e., the detail level) with which a system is described and on the class of networks considered. We keep fixed the space axes (that is, nodes’ coordinates) and we isolated the dynamics behind networks evolution process. In particular, our goal is to detect new instincts that trigger online social networks utilization and spread the adoption of novel communities. We formalized this enhanced social network evolution by adopt- ing new special nodes, called “sirens” that, thanks to their ability to attract new links, were also able to construct efficient connection patterns. We both simulate the dynamics of individuals and sirens by considering three most known growth models, namely random, preferential attachment and social. Applying this new framework to real and synthetic social networks, we have shown that the sirens, even when used for a limited period of time, effectively shrink the time needed to get a network in mature state. In order to provide a concrete context of our findings, we formalized the cost of setting up such enhancement and we provided the best combinations of system’s parameters, such as number of sirens, time span of utilization and attractiveness, which minimize this cost. 5 6 Contents Abstract 5 1 Introduction 15 1.1 Outlineofthethesis ............................ 17 2 Background 21 2.1 GraphTheory................................ 21 2.1.1 Statisticalproperties. 24 2.2 NetworkModeling ............................. 27 2.2.1 Erd˝os-R´enyiNetworks . 28 2.2.2 SmallWorldNetworks....................... 29 2.2.3 Scale-FreeNetworks .. .. .. .. .. .. .. .. .. .. 31 2.3 Longitudinalanalysis............................ 34 2.4 Critical analysis of complex networks . 36 2.4.1 Staticrobustness .......................... 36 2.4.2 Dynamicalrobustness . .. .. .. .. .. .. .. .. .. 38 2.5 Spreadingprocesses ............................ 39 2.5.1 Epidemic spreading . 39 2.5.2 Rumorsspreading ......................... 40 3 Multidimensionalanalysisofnetworks 43 4 The space dimension 47 4.1 Relatedwork ................................ 49 4.2 TelescopicAlgorithm............................ 51 4.3 ExperimentalResults............................ 54 4.3.1 Datasets............................... 54 4.3.2 Results ............................... 63 4.4 Networkperturbations .. .. .. .. .. .. .. .. .. .. .. 74 4.4.1 Null models as a function of networks’ size . 83 4.4.2 Nullmodelandrealnetworkscomparison . 91 7 8 CONTENTS 5 The time dimension 95 5.1 Introduction................................. 95 5.2 Networkgrowthmodels .......................... 96 5.2.1 Evolutionary models: serial and parallel . 98 5.3 Sirens .................................... 100 5.3.1 NetworkCost............................ 103 5.4 ExperimentalResults. .. .. .. .. .. .. .. .. .. .. .. 103 5.4.1 Datasets............................... 104 5.4.2 Results ............................... 108 6 Conclusions and Future Directions 127 A Supplementarymaterialforspacedimension 133 List of Figures 2.1 Graph abstraction of the seven bridges of K¨onigsberg problem. .... 22 2.2 Graphtypes. ................................ 23 2.3 Regular to random networks via small-world . 30 2.4 Random and scale-free degree distributions . 32 2.5 Impact of cumulative degree distribution on noise reduction in the data 33 2.6 Randomfailureandattacksonnetworks . 37 3.1 High level overview of multidimensional analysis on complex networks 44 4.1 Example of telescopic analysis . 48 4.2 Exampleofbox-coveringonnetworks. 49 4.3 Spatial coarse graining for brain voxels . 50 4.4 One-stepabstractionprocess . 52 4.5 Sequence of box covering iteration for telescopic analysis . ... 53 4.6 Boxcoveringissue ............................. 53 4.7 Exampleofboxcoveringonrealnetworks . 55 4.8 Subwaynetworkmaps ........................... 56 4.9 Examplesofcity-basednetworks . 57 4.10 Exampleofcity-basednetworks. 58 4.11 Pcum distribution of subways, transportation and social networks. 62 4.12 Number of collapsed nodes and edges as a function of f in log-log axes 64 4.13 Effect of the telescopic abstraction on the diameter as a function of f 65 4.14 Effect of the telescopic analysis on the degree . 66 4.15 Impact of the telescopic analysis on the degree correlations ρ ..... 67 4.16 Effect of the telescopic process on Eglob ................. 68 4.17 Effect of the telescopic analysis on Eloc .................. 70 4.18 Effect of the telescopic analysis on ct and cm .............. 71 4.19 Effect of the telescopic analysis on C/Eglob ............... 72 4.20 Effect of the abstraction process on the degree distribution P (k) ... 73 4.21 Number of collapsed nodes and edges with different perturbations .. 75 4.22 Telescopic analysis on various degree based indexes and perturbations 77 4.23 Effect of the telescopic analysis on ρ with different perturbations . 78 4.24 Effect of the telescopic process on Eglob as a function of perturbations 79 9 10 LIST OF FIGURES 4.25 Effect of the telescopic process on Eloc as a function of perturbations . 80 4.26 How different perturbations influence costs in the telescopic analysis . 81 4.27 How different perturbations influence C/Eglob in the telescopic analysis 82 4.28 Effect of the telescopic analysis on random networks on nodes and edges 84 4.29 Effect of the telescopic abstraction on diameter on null models . ... 85 4.30 Effect of the telescopic abstraction on degree in null models . ... 86 4.31 Effect of the telescopic abstraction on Eglob onrandomnetworks . 87 4.32 Effect of the telescopic abstraction on Eloc ................ 88 4.33 Effect of the telescopic analysis on ct and cm in randomnetworks. 89 4.34 How the telescopic analysis influence C/Eglob .............. 90 4.35 Comparison between original and randomized subway networks .... 92 4.36 Comparison between original and randomized city-based networks .. 93 5.1 Example of social rule in evolving social networks . 98 5.2 Impact of C scale parameter on overall simulated time . 99 5.3 Example of on line social network with sirens . 100 5.4 Examples of sirens’ social rule . 102 5.5 Cumulative degree distributions of CM and VT datasets . 106 5.6 NodedegreecorrelationsinCMandVTdatasets . 106 5.7 Serialnetworkgrowthmodel . 110 5.8 Parallelnetworkgrowthmodel . 111 5.9 Parallel network growth model of random networks . 111 5.10 How attractiveness influence the overall network behavior . ..... 113 5.11 Eglob varying the cost, accelerated growth, word of mouth model (CM) 114 5.12 Eglob varying the cost, accelerated growth, preferential model (CM) . 115 5.13 Effect of multiple runs of simulations on VT . 115 5.14 How cost affects Eglob in accelerated growth, broadcast model (VT) . 116 5.15 How cost affects Eglob in accelerated growth, word of mouth model(VT)117 5.16 How cost affects Eglob in accelerated growth, preferential model (VT) 118 5.17 Eglob as a function of cost keeping fixed the number of sirens (CM) . 120 5.18 Effect of attractiveness and time in CM, word of mouth model . 120 5.19 Effect of attractiveness and time in CM, preferential model . .... 121 5.20 Effect of attractiveness and time in VT, broadcast model . 121 5.21 Effect of attractiveness and time in VT, word of mouth model . 122 5.22 Effect of attractiveness and time in VT, preferential model . .... 122 5.23 Correlation between cost and Tmin inCM................ 124 5.24 Correlation between cost and Tmin inVT ................ 125 A.1 Effect of the telescopic analysis on nodes of perturbed subways .... 134 A.2 Effect of the telescopic analysis on edges of perturbed subways .... 135 A.3 Effect of the telescopic analysis on kmax of perturbed subways. 136 A.4 Effect of the telescopic analysis on kmean of perturbed subways . 137 A.5 Effect of the telescopic analysis on σk of perturbed subways . 138 A.6 Effect of the telescopic analysis on Eglob of perturbed subways. 139 A.7 Effect of the telescopic analysis on Eglob of perturbed subways. 140 LIST OF FIGURES 11 A.8 Effect of the telescopic
Load more

Recommended publications
  • Opinion-Based Centrality in Multiplex Networks: a Convex Optimization Approach
    Opinion-Based Centrality in Multiplex Networks: A Convex Optimization Approach Alexandre Reiffers-Masson and Vincent Labatut Laboratoire Informatique d'Avignon (LIA) EA 4128 Universit´ed'Avignon, France June 12, 2017 Abstract Most people simultaneously belong to several distinct social networks, in which their relations can be different. They have opinions about certain topics, which they share and spread on these networks, and are influenced by the opinions of other persons. In this paper, we build upon this observation to propose a new nodal centrality measure for multiplex networks. Our measure, called Opinion centrality, is based on a stochastic model representing opinion propagation dynamics in such a network. We formulate an optimization problem consisting in maximizing the opinion of the whole network when controlling an external influence able to affect each node individually. We find a mathematical closed form of this problem, and use its solution to derive our centrality measure. According to the opinion centrality, the more a node is worth investing external influence, and the more it is central. We perform an empirical study of the proposed centrality over a toy network, as well as a collection of real-world networks. Our measure is generally negatively correlated with existing multiplex centrality measures, and highlights different types of nodes, accordingly to its definition. Cite as: A. Reiffers-Masson & V. Labatut. Opinion-based Centrality in Multiplex Networks. Network Science, 5(2):213-234, 2017. Doi: 10.1017/nws.2017.7 1 Introduction In our ultra-connected world, many people simultaneously belong to several distinct social networks, in which their relations can be different.
    [Show full text]
  • Modeling Customer Preferences Using Multidimensional Network Analysis in Engineering Design
    Modeling customer preferences using multidimensional network analysis in engineering design Mingxian Wang1, Wei Chen1, Yun Huang2, Noshir S. Contractor2 and Yan Fu3 1 Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA 2 Science of Networks in Communities, Northwestern University, Evanston, IL 60208, USA 3 Global Data Insight and Analytics, Ford Motor Company, Dearborn, MI 48121, USA Abstract Motivated by overcoming the existing utility-based choice modeling approaches, we present a novel conceptual framework of multidimensional network analysis (MNA) for modeling customer preferences in supporting design decisions. In the proposed multidimensional customer–product network (MCPN), customer–product interactions are viewed as a socio-technical system where separate entities of `customers' and `products' are simultaneously modeled as two layers of a network, and multiple types of relations, such as consideration and purchase, product associations, and customer social interactions, are considered. We first introduce a unidimensional network where aggregated customer preferences and product similarities are analyzed to inform designers about the implied product competitions and market segments. We then extend the network to a multidimensional structure where customer social interactions are introduced for evaluating social influence on heterogeneous product preferences. Beyond the traditional descriptive analysis used in network analysis, we employ the exponential random graph model (ERGM) as a unified statistical
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Design and Analysis of Graph-Based Codes Using Algebraic Lifts and Decoding Networks Allison Beemer University of Nebraska - Lincoln, [email protected]
    University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Dissertations, Theses, and Student Research Papers Mathematics, Department of in Mathematics 3-2018 Design and Analysis of Graph-based Codes Using Algebraic Lifts and Decoding Networks Allison Beemer University of Nebraska - Lincoln, [email protected] Follow this and additional works at: https://digitalcommons.unl.edu/mathstudent Part of the Discrete Mathematics and Combinatorics Commons, and the Other Mathematics Commons Beemer, Allison, "Design and Analysis of Graph-based Codes Using Algebraic Lifts nda Decoding Networks" (2018). Dissertations, Theses, and Student Research Papers in Mathematics. 87. https://digitalcommons.unl.edu/mathstudent/87 This Article is brought to you for free and open access by the Mathematics, Department of at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Dissertations, Theses, and Student Research Papers in Mathematics by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. DESIGN AND ANALYSIS OF GRAPH-BASED CODES USING ALGEBRAIC LIFTS AND DECODING NETWORKS by Allison Beemer A DISSERTATION Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfilment of Requirements For the Degree of Doctor of Philosophy Major: Mathematics Under the Supervision of Professor Christine A. Kelley Lincoln, Nebraska March, 2018 DESIGN AND ANALYSIS OF GRAPH-BASED CODES USING ALGEBRAIC LIFTS AND DECODING NETWORKS Allison Beemer, Ph.D. University of Nebraska, 2018 Adviser: Christine A. Kelley Error-correcting codes seek to address the problem of transmitting information effi- ciently and reliably across noisy channels. Among the most competitive codes developed in the last 70 years are low-density parity-check (LDPC) codes, a class of codes whose structure may be represented by sparse bipartite graphs.
    [Show full text]
  • The Key Technology of Computer Network Vulnerability Assessment Based on Neural Network
    Wang J Wireless Com Network (2020) 2020:222 https://doi.org/10.1186/s13638-020-01841-y RESEARCH Open Access The key technology of computer network vulnerability assessment based on neural network Shaoqiang Wang* *Correspondence: [email protected] Abstract School of Computer With the wide application of computer network, network security has attracted more Science and Technology, Changchun University, and more attention. The main reason why all kinds of attacks on the network can pose Changchun 130022, Jilin, a great threat to the network security is the vulnerability of the computer network China system itself. Introducing neural network technology into computer network vulner- ability assessment can give full play to the advantages of neural network in network vulnerability assessment. The purpose of this article is by organizing feature map neural network, and the combination of multilayer feedforward neural network, the training samples using SOM neural network clustering, the result of clustering are added to the original training samples and set a certain weight, based on the weighted iterative update ceaselessly, in order to improve the convergence speed of BP neural network. On the BP neural network, algorithm for LM algorithm was improved, the large matrix inversion in the LM algorithm using the parallel algorithm method is improved for solving system of linear equations, and use of computer network vulnerability assess- ment as the computer simulation and analysis on the actual example designs a set of computer network vulnerability assessment scheme, fnally the vulnerability is lower than 0.75, which is benefcial to research on related theory and application to provide the reference and help.
    [Show full text]
  • Research Article Multilayer Network-Based Production Flow Analysis
    Hindawi Complexity Volume 2018, Article ID 6203754, 15 pages https://doi.org/10.1155/2018/6203754 Research Article Multilayer Network-Based Production Flow Analysis Tamás Ruppert, Gergely Honti, and János Abonyi MTA-PE “Lendület” Complex Systems Monitoring Research Group, Department of Process Engineering, University of Pannonia, Veszprém, Hungary Correspondence should be addressed to János Abonyi; [email protected] Received 24 February 2018; Revised 18 May 2018; Accepted 27 May 2018; Published 16 July 2018 Academic Editor: Dimitris Mourtzis Copyright © 2018 Tamás Ruppert et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. A multilayer network model for the exploratory analysis of production technologies is proposed. To represent the relationship between products, parts, machines, resources, operators, and skills, standardized production and product-relevant data are transformed into a set of bi- and multipartite networks. This representation is beneficial in production flow analysis (PFA) that is used to identify improvement opportunities by grouping similar groups of products, components, and machines. It is demonstrated that the goal-oriented mapping and modularity-based clustering of multilayer networks can serve as a readily applicable and interpretable decision support tool for PFA, and the analysis of the degrees and correlations of a node can identify critically important skills and resources. The applicability of the proposed methodology is demonstrated by a well-documented benchmark problem of a wire-harness production process. The results confirm that the proposed multilayer network can support the standardized integration of production-relevant data and exploratory analysis of strongly interconnected production systems.
    [Show full text]
  • Frequent Itemset Mining and Multi-Layer Network-Based Analysis of RDF Databases
    mathematics Article Frequent Itemset Mining and Multi-Layer Network-Based Analysis of RDF Databases Gergely Honti † and János Abonyi *,† MTA-PE Complex Systems Monitoring Research Group, University of Pannonia, 8200 Veszprem, Hungary; [email protected] * Correspondence: [email protected] † These authors contributed equally to this work. Abstract: Triplestores or resource description framework (RDF) stores are purpose-built databases used to organise, store and share data with context. Knowledge extraction from a large amount of interconnected data requires effective tools and methods to address the complexity and the underlying structure of semantic information. We propose a method that generates an interpretable multilayered network from an RDF database. The method utilises frequent itemset mining (FIM) of the subjects, predicates and the objects of the RDF data, and automatically extracts informative subsets of the database for the analysis. The results are used to form layers in an analysable multidimensional network. The methodology enables a consistent, transparent, multi-aspect-oriented knowledge extraction from the linked dataset. To demonstrate the usability and effectiveness of the methodology, we analyse how the science of sustainability and climate change are structured using the Microsoft Academic Knowledge Graph. In the case study, the FIM forms networks of disciplines to reveal the significant interdisciplinary science communities in sustainability and climate change. The constructed multilayer network then enables an analysis of the significant disciplines Citation: Honti, G.; Abonyi, J. and interdisciplinary scientific areas. To demonstrate the proposed knowledge extraction process, we Frequent Itemset Mining and search for interdisciplinary science communities and then measure and rank their multidisciplinary Multi-Layer Network-Based Analysis effects.
    [Show full text]
  • Do-It-Yourself Local Wireless Networks: a Multidimensional Network Analysis of Mobile Node Social Aspects
    Do-it-yourself Local Wireless Networks: A Multidimensional Network Analysis of Mobile Node Social Aspects Annalisa Socievole and Salvatore Marano DIMES, University of Calabria, Ponte P. Bucci, Rende (CS), Italy Keywords: Opportunistic Networks, Multi-layer Social Network, Mobility Trace Analytics, Wireless Encounters, Facebook. Abstract: The emerging paradigm of Do-it-yourself (DIY) networking is increasingly taking the attention of research community on DTNs, opportunistic networks and social networks since it allows the creation of local human- driven wireless networks outside the public Internet. Even when Internet is available, DIY networks may form an interesting alternative option for communication encouraging face-to-face interactions and more ambitious objectives such as e-participation and e-democracy. The aim of this paper is to analyze a set of mobility traces describing both local wireless interactions and online friendships in different networking environments in order to explore a fundamental aspect of these social-driven networks: node centrality. Since node centrality plays an important role in message forwarding, we propose a multi-layer network approach to the analysis of online and offline node centrality in DIY networks. Analyzing egocentric and sociocentric node centrality on the social network detected through wireless encounters and on the corresponding Facebook social network for 6 different real-world traces, we show that online and offline degree centralities are significantly correlated on most datasets. On the contrary, betweenness, closeness and eigenvector centralities show medium-low correlation values. 1 INTRODUCTION for example, an alternative option for communication is necessary. Do-it-yourself (DIY) networks (Antoniadis et al., DIY networks are intrinsically social-based due 2014) have been recently proposed new generation to human mobility and this feature is well suitable networks where a multitude of human-driven mo- for exchanging information in an ad hoc manner.
    [Show full text]
  • Interactive Visualization of a News Clips Network a Journalistic Research and Knowledge Discovery Tool
    Interactive Visualization of a News Clips Network A Journalistic Research and Knowledge Discovery Tool Jose´ Devezas and Alvaro´ Figueira CRACS/INESC TEC, Faculdade de Ciencias,ˆ Universidade do Porto Rua do Campo Alegre, 1021/1055, 4169-007 Porto, Portugal Keywords: Visualization, Networks, Communities, Interactive, Named Entities, News Clips. Abstract: Interactive visualization systems are powerful tools in the task of exploring and understanding data. We describe two implementations of this approach, where a multidimensional network of news clips is depicted by taking advantage of its community structure. The first implementation is a multiresolution map of news clips that uses topic detection both at the clip level and at the community level, in order to assign labels to the nodes in each resolution. The second implementation is a traditional force-directed network visualization with several additional interactive aspects that provide a rich user experience for knowledge discovery. We describe a common use case for the visualization systems as a journalistic research and knowledge discovery tool. Both systems illustrate the links between news clips, induced by the co-occurrence of named entities, as well as several metadata fields based on the information contained within each node. 1 INTRODUCTION Our goal was to create an interface for the user to explore the already available information in a user- Interactively exploring data through visualization en- friendly and insightful manner. We largely took ad- ables the users to improve their understanding of the vantage of the community structure of the news clips provided information. They gain knowledge by “con- network — identified by the Breadcrumbs system us- necting the dots”, establishing mental relationships ing methodologies such as the Louvain method (Blon- between the individual pieces of data in an intuitive del et al., 2008) or Tang’s multidimensional integra- manner.
    [Show full text]
  • Multidimensional Networks Félicité Gamgne Domgue, Norbert Tsopze, René Ndoundam
    Multidimensional networks Félicité Gamgne Domgue, Norbert Tsopze, René Ndoundam To cite this version: Félicité Gamgne Domgue, Norbert Tsopze, René Ndoundam. Multidimensional networks: A novel node centrality metric based on common neighborhood. CARI 2020 - Colloque Africain sur la Recherche en Informatique et en Mathématiques Appliquées, Oct 2020, Thiès, Senegal. hal-02934749 HAL Id: hal-02934749 https://hal.archives-ouvertes.fr/hal-02934749 Submitted on 9 Sep 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Multidimensional networks A novel node centrality metric based on common neighborhood Gamgne Domgue Félicité, Tsopze Norbert, Ndoundam René Sorbonne University, IRD, UMMISCO, F-93143, Bondy, France, University of Yaounde I Yaounde,Cameroon [email protected], [email protected] [email protected] [email protected] ABSTRACT. Complex networks have been receiving increasing attention by the scientific community. They can be represented by multidimensional networks in which there is multiple types of connec- tions between nodes. Thanks also to the increasing availability of analytical measures that have been extended in order to describe and analyze properties of entities involved in this kind of multiple re- lationship representation of networks.
    [Show full text]