DOCSLIB.ORG

Social Network Analysis of Researchers' Communication And

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Social Network Analysis of Researchers' Communication And University of South Florida Scholar Commons Graduate Theses and Dissertations Graduate School 6-16-2014 Social Network Analysis of Researchers' Communication and Collaborative Networks Using Self-reported Data Oguz Cimenler University of South Florida, [email protected] Follow this and additional works at: https://scholarcommons.usf.edu/etd Part of the Industrial Engineering Commons Scholar Commons Citation Cimenler, Oguz, "Social Network Analysis of Researchers' Communication and Collaborative Networks Using Self-reported Data" (2014). Graduate Theses and Dissertations. https://scholarcommons.usf.edu/etd/5201 This Dissertation is brought to you for free and open access by the Graduate School at Scholar Commons. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Scholar Commons. For more information, please contact [email protected]. Social Network Analysis of Researchers’ Communication and Collaborative Networks Using Self-reported Data by Oguz Cimenler A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Industrial and Management Systems Engineering College of Engineering University of South Florida Major Professor: Kingsley A. Reeves, Jr., Ph.D. José Zayas-Castro, Ph.D. Alex Savachkin, Ph.D. Adriana Iamnitchi, Ph.D. John Skvoretz, Ph.D. Date of Approval: June 16, 2014 Keywords: Informetrics, individual innovativeness, exponential random graph models (ERGMs), Poisson regression analysis, structural equation modeling Copyright © 2014, Oguz Cimenler DEDICATION I dedicate this dissertation to my adorable wife, Ummuhan Cimenler, who was always supportive to me during my study, to my beloved parents, Cumali and Emine Cimenler, particularly to my father, Cumali Cimenler, who is currently struggling with lung cancer, and to my dear brother, Omer Cimenler. ACKNOWLEDGMENTS I cannot express the degree of my gratitude to my adviser, Dr. Kingsley A. Reeves, who has encouraged and supported me at the times that I most needed. To prosper in my study, he always challenged me in a positive and constructive way, which was neither obstructive nor demanding. I am very fortunate to have such an important scholar in the field of Social Network Analysis as Dr. John Skvoretz, on my dissertation committee. He was always helpful and guided me with his key and fruitful comments about my research. I would like to thank Dr. Alex Savachkin, Dr. Jose Zayas-Castro, and Dr. Adriana Iamnitchi for serving on my research committee and giving me valuable comments and feedbacks during the development stages of my research. Especially, I would like to make special thank you to Dr. Alex Savachkin for his motivational support during course of my study. I also would like thank to all of tenured and tenured-track faculty members in the University of South Florida’s College of Engineering. They have dedicated their time, energy and effort by filling out to my questionnaire which was one of the core tools I used in my study. Particularly, I would like to thank to Dr. Ali Yalcin and Dr. Gokhan Mumcu for their feedbacks and comments during the pilot-study stage of my questionnaire. Finally, I would like to thank to Dr. John Kuhn for being the chair of my dissertation defense meeting and Ms. Catherine Burton and Dr. Anna Dixon for her patience and corrections on my dissertation draft. TABLE OF CONTENTS LIST OF TABLES ......................................................................................................................... iii LIST OF FIGURES .........................................................................................................................v ABSTRACT ................................................................................................................................... vi CHAPTER 1: INTRODUCTION ....................................................................................................1 1.1. Statement of the Research Problem ..............................................................................1 1.2. Proposed Solution .........................................................................................................7 1.3. Statement of Research Objectives ..............................................................................10 CHAPTER 2: AN EVALUATION OF COLLABORATIVE RESEARCH IN A COLLEGE OF ENGINEERING: A SOCIAL NETWORK APPROACH.................................................15 2.1. Introduction .................................................................................................................15 2.2. Literature Review and Hypotheses .............................................................................16 2.2.1. The Field of Informetrics .............................................................................16 2.2.2. Scientific Collaboration ...............................................................................17 2.2.3. Relationship between Researchers’ Communication and Their Collaborative Outputs ..................................................................................18 2.2.4. Relationship between Researchers’ Demographic Attributes and Their Collaborative Outputs ..................................................................................21 2.3. Method ........................................................................................................................23 2.3.1. Sample and Questionnaire ...........................................................................23 2.3.2. Data Collection ............................................................................................26 2.3.3. Constructing Social Network Data Matrixes ...............................................28 2.4. Results .........................................................................................................................30 2.4.1. Visual Inspection of Networks.....................................................................30 2.4.2. Statistical and Descriptive Properties of Networks .....................................31 2.4.3. Network Comparisons .................................................................................38 2.4.4. Network Prediction ......................................................................................40 2.4.5. Centrality Comparisons ...............................................................................54 2.5. Discussion ...................................................................................................................59 i CHAPTER 3: A REGRESSION ANALYSIS OF RESEARCHERS’ SOCIAL NETWORK METRICS ON THEIR CITATION PERFORMANCE IN A COLLEGE OF ENGINEERING ......................................................................................................................69 3.1. Introduction .................................................................................................................69 3.2. Literature Review and Hypotheses .............................................................................70 3.2.1. A Performance Measure of Researchers: h-index .......................................70 3.2.2. Social Network Metrics ...............................................................................71 3.3. Method ........................................................................................................................75 3.3.1. Constructing Data Sets for Statistical Model ...............................................75 3.3.2. Poisson Regression Model ...........................................................................76 3.4. Results .........................................................................................................................77 3.5. Discussion ...................................................................................................................80 CHAPTER 4: A STRUCTURAL EQUATION MODEL TO TEST THE IMPACT OF RESEARCHERS’ INDIVIDUAL INNOVATIVENESS ON THEIR COLLABORATIVE OUTPUTS .............................................................................................85 4.1. Introduction .................................................................................................................85 4.2. Literature Review and Hypotheses .............................................................................86 4.2.1. The Effect of Individual Innovativeness (Iinnov) on Researchers’ Collaborative Outputs (CO) .........................................................................86 4.2.2. Tie Strength of an Individual to Other Conversational Partners (TS) .........92 4.3. Method ........................................................................................................................94 4.3.1. Constructing Dataset for Statistical Model ..................................................94 4.3.2. Statistical Model ..........................................................................................95 4.4. Results .........................................................................................................................95 4.4.1. Partial Least Squares (PLS) Path Models ....................................................95 4.4.2. Analysis of Partial Least Squares (PLS) Models .........................................98 4.4.2.1. Assessment of Measurement Models............................................98 4.4.2.2. Assessment of Structural Models................................................100 4.5. Discussion .................................................................................................................104
Load more

Recommended publications
  • Robustness and Assortativity for Diffusion-Like Processes in Scale
    epl draft Robustness and Assortativity for Diffusion-like Processes in Scale- free Networks G. D’Agostino1, A. Scala2,3, V. Zlatic´4 and G. Caldarelli5,3 1 ENEA - CR ”Casaccia” - Via Anguillarese 301 00123, Roma - Italy 2 CNR-ISC and Department of Physics, University of Rome “Sapienza” P.le Aldo Moro 5 00185 Rome, Italy 3 London Institute of Mathematical Sciences, 22 South Audley St Mayfair London W1K 2NY, UK 4 Theoretical Physics Division, Rudjer Boˇskovi´cInstitute, P.O.Box 180, HR-10002 Zagreb, Croatia 5 IMT Lucca Institute for Advanced Studies, Piazza S. Ponziano 6, Lucca, 55100, Italy PACS 89.75.Hc – Networks and genealogical trees PACS 05.70.Ln – Nonequilibrium and irreversible thermodynamics PACS 87.23.Ge – Dynamics of social systems Abstract – By analysing the diffusive dynamics of epidemics and of distress in complex networks, we study the effect of the assortativity on the robustness of the networks. We first determine by spectral analysis the thresholds above which epidemics/failures can spread; we then calculate the slowest diffusional times. Our results shows that disassortative networks exhibit a higher epidemi- ological threshold and are therefore easier to immunize, while in assortative networks there is a longer time for intervention before epidemic/failure spreads. Moreover, we study by computer sim- ulations the sandpile cascade model, a diffusive model of distress propagation (financial contagion). We show that, while assortative networks are more prone to the propagation of epidemic/failures, degree-targeted immunization policies increases their resilience to systemic risk. Introduction. – The heterogeneity in the distribu- propagation of an epidemic [13–15].
    [Show full text]
  • Ollivier Ricci Curvature of Directed Hypergraphs Marzieh Eidi1* & Jürgen Jost1,2
    www.nature.com/scientificreports OPEN Ollivier Ricci curvature of directed hypergraphs Marzieh Eidi1* & Jürgen Jost1,2 Many empirical networks incorporate higher order relations between elements and therefore are naturally modelled as, possibly directed and/or weighted, hypergraphs, rather than merely as graphs. In order to develop a systematic tool for the statistical analysis of such hypergraph, we propose a general defnition of Ricci curvature on directed hypergraphs and explore the consequences of that defnition. The defnition generalizes Ollivier’s defnition for graphs. It involves a carefully designed optimal transport problem between sets of vertices. While the defnition looks somewhat complex, in the end we shall be able to express our curvature in a very simple formula, κ = µ0 − µ2 − 2µ3 . This formula simply counts the fraction of vertices that have to be moved by distances 0, 2 or 3 in an optimal transport plan. We can then characterize various classes of hypergraphs by their curvature. Principles of network analysis. Network analysis constitutes one of the success stories in the study of complex systems3,6,11. For the mathematical analysis, a network is modelled as a (perhaps weighted and/or directed) graph. One can then look at certain graph theoretical properties of an empirical network, like its degree or motiv distribution, its assortativity or clustering coefcient, the spectrum of its Laplacian, and so on. One can also compare an empirical network with certain deterministic or random theoretical models. Successful as this analysis clearly is, we nevertheless see two important limitations. One is that many of the prominent concepts and quantities used in the analysis of empirical networks are node based, like the degree sequence.
    [Show full text]
  • Assortativity and Mixing
    Assortativity and Assortativity and Mixing General mixing between node categories Mixing Assortativity and Mixing Definition Definition I Assume types of nodes are countable, and are Complex Networks General mixing General mixing Assortativity by assigned numbers 1, 2, 3, . Assortativity by CSYS/MATH 303, Spring, 2011 degree degree I Consider networks with directed edges. Contagion Contagion References an edge connects a node of type µ References e = Pr Prof. Peter Dodds µν to a node of type ν Department of Mathematics & Statistics Center for Complex Systems aµ = Pr(an edge comes from a node of type µ) Vermont Advanced Computing Center University of Vermont bν = Pr(an edge leads to a node of type ν) ~ I Write E = [eµν], ~a = [aµ], and b = [bν]. I Requirements: X X X eµν = 1, eµν = aµ, and eµν = bν. µ ν ν µ Licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License. 1 of 26 4 of 26 Assortativity and Assortativity and Outline Mixing Notes: Mixing Definition Definition General mixing General mixing Assortativity by I Varying eµν allows us to move between the following: Assortativity by degree degree Definition Contagion 1. Perfectly assortative networks where nodes only Contagion References connect to like nodes, and the network breaks into References subnetworks. General mixing P Requires eµν = 0 if µ 6= ν and µ eµµ = 1. 2. Uncorrelated networks (as we have studied so far) Assortativity by degree For these we must have independence: eµν = aµbν . 3. Disassortative networks where nodes connect to nodes distinct from themselves. Contagion I Disassortative networks can be hard to build and may require constraints on the eµν.
    [Show full text]
  • Evolution Leads to Emergence: an Analysis of Protein
    bioRxiv preprint doi: https://doi.org/10.1101/2020.05.03.074419; this version posted May 3, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 2 3 Evolution leads to emergence: An analysis of protein interactomes 4 across the tree of life 5 6 7 8 Erik Hoel1*, Brennan Klein2,3, Anshuman Swain4, Ross Grebenow5, Michael Levin1 9 10 1 Allen Discovery Center at Tufts University, Medford, MA, USA 11 2 Network Science Institute, Northeastern University, Boston, MA, USA 12 3 Laboratory for the Modeling of Biological & Sociotechnical Systems, Northeastern University, 13 Boston, USA 14 4 Department of Biology, University of Maryland, College Park, MD, USA 15 5 Drexel University, Philadelphia, PA, USA 16 17 * Author for Correspondence: 18 200 Boston Ave., Suite 4600 19 Medford, MA 02155 20 e-mail: [email protected] 21 22 Running title: Evolution leads to higher scales 23 24 Keywords: emergence, information, networks, protein, interactomes, evolution 25 26 27 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.03.074419; this version posted May 3, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 2 28 Abstract 29 30 The internal workings of biological systems are notoriously difficult to understand.
    [Show full text]
  • (2020) Small Worlds and Clustering in Spatial Networks
    PHYSICAL REVIEW RESEARCH 2, 023040 (2020) Small worlds and clustering in spatial networks Marián Boguñá ,1,2,* Dmitri Krioukov,3,4,† Pedro Almagro ,1,2 and M. Ángeles Serrano1,2,5,‡ 1Departament de Física de la Matèria Condensada, Universitat de Barcelona, Martí i Franquès 1, E-08028 Barcelona, Spain 2Universitat de Barcelona Institute of Complex Systems (UBICS), Universitat de Barcelona, Barcelona, Spain 3Network Science Institute, Northeastern University, 177 Huntington avenue, Boston, Massachusetts 022115, USA 4Department of Physics, Department of Mathematics, Department of Electrical & Computer Engineering, Northeastern University, 110 Forsyth Street, 111 Dana Research Center, Boston, Massachusetts 02115, USA 5Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, E-08010 Barcelona, Spain (Received 20 August 2019; accepted 12 March 2020; published 14 April 2020) Networks with underlying metric spaces attract increasing research attention in network science, statistical physics, applied mathematics, computer science, sociology, and other fields. This attention is further amplified by the current surge of activity in graph embedding. In the vast realm of spatial network models, only a few repro- duce even the most basic properties of real-world networks. Here, we focus on three such properties—sparsity, small worldness, and clustering—and identify the general subclass of spatial homogeneous and heterogeneous network models that are sparse small worlds and that have nonzero clustering in the thermodynamic
    [Show full text]
  • Extraction and Analysis of Facebook Friendship Relations
    Chapter 12 Extraction and Analysis of Facebook Friendship Relations Salvatore Catanese, Pasquale De Meo, Emilio Ferrara, Giacomo Fiumara, and Alessandro Provetti Abstract Online social networks (OSNs) are a unique web and social phenomenon, affecting tastes and behaviors of their users and helping them to maintain/create friendships. It is interesting to analyze the growth and evolution of online social networks both from the point of view of marketing and offer of new services and from a scientific viewpoint, since their structure and evolution may share similarities with real-life social networks. In social sciences, several techniques for analyzing (off-line) social networks have been developed, to evaluate quantitative properties (e.g., defining metrics and measures of structural characteristics of the networks) or qualitative aspects (e.g., studying the attachment model for the network evolution, the binary trust relationships, and the link prediction problem). However, OSN analysis poses novel challenges both to computer and Social scientists. We present our long-term research effort in analyzing Facebook, the largest and arguably most successful OSN today: it gathers more than 500 million users. Access to data about Facebook users and their friendship relations is restricted; thus, we acquired the necessary information directly from the front end of the website, in order to reconstruct a subgraph representing anonymous interconnections among a significant subset of users. We describe our ad hoc, privacy-compliant crawler for Facebook data extraction. To minimize bias, we adopt two different graph mining techniques: breadth-first-search (BFS) and rejection sampling. To analyze the structural properties of samples consisting of millions of nodes, we developed a S.Catanese•P.DeMeo•G.Fiumara Department of Physics, Informatics Section.
    [Show full text]
  • The Perceived Assortativity of Social Networks: Methodological Problems and Solutions
    The perceived assortativity of social networks: Methodological problems and solutions David N. Fisher1, Matthew J. Silk2 and Daniel W. Franks3 Abstract Networks describe a range of social, biological and technical phenomena. An important property of a network is its degree correlation or assortativity, describing how nodes in the network associate based on their number of connections. Social networks are typically thought to be distinct from other networks in being assortative (possessing positive degree correlations); well-connected individuals associate with other well-connected individuals, and poorly-connected individuals associate with each other. We review the evidence for this in the literature and find that, while social networks are more assortative than non-social networks, only when they are built using group-based methods do they tend to be positively assortative. Non-social networks tend to be disassortative. We go on to show that connecting individuals due to shared membership of a group, a commonly used method, biases towards assortativity unless a large enough number of censuses of the network are taken. We present a number of solutions to overcoming this bias by drawing on advances in sociological and biological fields. Adoption of these methods across all fields can greatly enhance our understanding of social networks and networks in general. 1 1. Department of Integrative Biology, University of Guelph, Guelph, ON, N1G 2W1, Canada. Email: [email protected] (primary corresponding author) 2. Environment and Sustainability Institute, University of Exeter, Penryn Campus, Penryn, TR10 9FE, UK Email: [email protected] 3. Department of Biology & Department of Computer Science, University of York, York, YO10 5DD, UK Email: [email protected] Keywords: Assortativity; Degree assortativity; Degree correlation; Null models; Social networks Introduction Network theory is a useful tool that can help us explain a range of social, biological and technical phenomena (Pastor-Satorras et al 2001; Girvan and Newman 2002; Krause et al 2007).
    [Show full text]
  • Assortativity Measures for Weighted and Directed Networks
    Assortativity measures for weighted and directed networks Yelie Yuan1, Jun Yan1, and Panpan Zhang2,∗ 1Department of Statistics, University of Connecticut, Storrs, CT 06269 2Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104 ∗Corresponding author: [email protected] January 15, 2021 arXiv:2101.05389v1 [stat.AP] 13 Jan 2021 Abstract Assortativity measures the tendency of a vertex in a network being connected by other ver- texes with respect to some vertex-specific features. Classical assortativity coefficients are defined for unweighted and undirected networks with respect to vertex degree. We propose a class of assortativity coefficients that capture the assortative characteristics and structure of weighted and directed networks more precisely. The vertex-to-vertex strength correlation is used as an example, but the proposed measure can be applied to any pair of vertex-specific features. The effectiveness of the proposed measure is assessed through extensive simula- tions based on prevalent random network models in comparison with existing assortativity measures. In application World Input-Ouput Networks, the new measures reveal interesting insights that would not be obtained by using existing ones. An implementation is publicly available in a R package wdnet. 1 Introduction In traditional network analysis, assortativity or assortative mixing (Newman, 2002) is a mea- sure assessing the preference of a vertex being connected (by edges) with other vertexes in a network. The measure reflects the principle of homophily (McPherson et al., 2001)|the tendency of the entities to be associated with similar partners in a social network. The prim- itive assortativity measure proposed by Newman(2002) was defined to study the tendency of connections between nodes based on their degrees, which is why it is also called degree-degree correlation (van der Hofstad and Litvak, 2014).
    [Show full text]
  • Spatial Analysis and Modeling of Urban Transportation Networks
    Spatial analysis and modeling of urban transportation networks Jingyi Lin Doctoral Thesis in Geodesy and Geoinformatics with Specialization in Geoinformatics Royal Institute of Technology Stockholm, Sweden, 2017 TRITA SoM 2017-15 ISRN KTH/SoM/2017-15/SE ISBN 978-91-7729-613-3 Doctoral Thesis Royal Institute of Technology (KTH) Department of Urban Planning and Environment Division of Geodesy and Geoinformatics SE-100 44 Stockholm Sweden © Jingyi Lin, 2017 Printed by US AB, Sweden 2017 Abstract Transport systems in general, and urban transportation systems in particular, are the backbone of a country or a city, therefore play an intrinsic role in the socio- economic development. There have been numerous studies on real transportation systems from multiple fileds, including geography, urban planning, and engineering. Geographers have extensively investigated transportation systems, and transport geography has developed as an important branch of geography with various studies on system structure, efficiency optimization, and flow distribution. However, the emergence of complex network theory provided a brand-new perspective for geographers and other researchers; therefore, it invoked more widespread interest in exploring transportation systems that present a typical node-link network structure. This trend inspires the author and, to a large extent, constitutes the motivation of this thesis. The overall objective of this thesis is to study and simulate the structure and dynamics of urban transportation systems, including aviation systems and ground transportation systems. More specificically, topological features, geometric properties, and dynamic evolution processes are explored and discussed in this thesis. To illustrate different construction mechanisms, as well as distinct evolving backgrounds of aviation systems and ground systems, China’s aviation network, U.S.
    [Show full text]
  • Modularity and Dynamics on Complex Networks
    Modularity AND Dynamics ON CompleX Networks Cambridge Elements DOI: 10.xxxx/xxxxxxxx (do NOT change) First PUBLISHED online: MMM DD YYYY (do NOT change) Renaud Lambiotte University OF OxforD Michael T. Schaub RWTH Aachen University Abstract: CompleX NETWORKS ARE TYPICALLY NOT homogeneous, AS THEY TEND TO DISPLAY AN ARRAY OF STRUCTURES AT DIffERENT scales. A FEATURE THAT HAS ATTRACTED A LOT OF RESEARCH IS THEIR MODULAR ORganisation, i.e., NETWORKS MAY OFTEN BE CONSIDERED AS BEING COMPOSED OF CERTAIN BUILDING blocks, OR modules. IN THIS book, WE DISCUSS A NUMBER OF WAYS IN WHICH THIS IDEA OF MODULARITY CAN BE conceptualised, FOCUSING SPECIfiCALLY ON THE INTERPLAY BETWEEN MODULAR NETWORK STRUCTURE AND DYNAMICS TAKING PLACE ON A network. WE discuss, IN particular, HOW MODULAR STRUCTURE AND SYMMETRIES MAY IMPACT ON NETWORK DYNAMICS and, VICE versa, HOW OBSERVATIONS OF SUCH DYNAMICS MAY BE USED TO INFER THE MODULAR STRUCTURe. WE ALSO REVISIT SEVERAL OTHER NOTIONS OF MODULARITY THAT HAVE BEEN PROPOSED FOR COMPLEX NETWORKS AND SHOW HOW THESE CAN BE RELATED TO AND INTERPRETED FROM THE POINT OF VIEW OF DYNAMICAL PROCESSES ON networks. SeVERAL REFERENCES AND POINTERS FOR FURTHER DISCUSSION AND FUTURE WORK SHOULD INFORM PRACTITIONERS AND RESEARchers, AND MAY MOTIVATE FURTHER STUDIES IN THIS AREA AT THE CORE OF Network Science. KeYWORds: Network Science; modularity; dynamics; COMMUNITY detection; BLOCK models; NETWORK clustering; GRAPH PARTITIONS JEL CLASSIfications: A12, B34, C56, D78, E90 © Renaud Lambiotte, Michael T. Schaub, 2021 ISBNs: xxxxxxxxxxxxx(PB)
    [Show full text]
  • Configuration Models As an Urn Problem
    Conguration Models as an Urn Problem: The Generalized Hypergeometric Ensemble of Random Graphs Giona Casiraghi ( [email protected] ) ETH Zurich Vahan Nanumyan ETH Zurich Research Article Keywords: fundamental issue of network data science, Monte-Carlo simulations, closed-form probability distribution Posted Date: March 5th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-254843/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Configuration models as an urn problem: the generalized hypergeometric ensemble of random graphs Giona Casiraghi1,* and Vahan Nanumyan1 1Chair of Systems Design, ETH Zurich,¨ Zurich,¨ 8092, Switzerland *[email protected] ABSTRACT A fundamental issue of network data science is the ability to discern observed features that can be expected at random from those beyond such expectations. Configuration models play a crucial role there, allowing us to compare observations against degree-corrected null-models. Nonetheless, existing formulations have limited large-scale data analysis applications either because they require expensive Monte-Carlo simulations or lack the required flexibility to model real-world systems. With the generalized hypergeometric ensemble, we address both problems. To achieve this, we map the configuration model to an urn problem, where edges are represented as balls in an appropriately constructed urn. Doing so, we obtain a random graph model reproducing and extending the properties of standard configuration models, with the critical advantage of a closed-form probability distribution. Introduction Essential features of complex systems are inferred by studying the deviations of empirical observations from suitable stochastic models. Network models, in particular, have become the state of the art for complex systems analysis, where systems’ constituents are represented as vertices, and their interactions are viewed as edges and modelled by means of edge probabilities in a random graph.
    [Show full text]
  • Edge Direction and the Structure of Networks
    Edge direction and the structure of networks Jacob G. Foster1, David V. Foster, Peter Grassberger, and Maya Paczuski Complexity Science Group, Department of Physics and Astronomy, University of Calgary, 2500 University Drive NW, Calgary, AB, Canada T2N 1N4 Edited by H. Eugene Stanley, Boston University, Boston, MA, and approved April 6, 2010 (received for review November 2, 2009) Directed networks are ubiquitous and are necessary to represent In directed networks, an edge from source to target (A → B) complex systems with asymmetric interactions—from food webs represents an asymmetric interaction; for example, that Web site to the World Wide Web. Despite the importance of edge direction A contains a hyperlink to Web site B, or organism A is eaten for detecting local and community structure, it has been disregarded by organism B. Edge direction is essential to evaluate and explain in studying a basic type of global diversity in networks: the tendency local structure in such networks. For instance, motif analysis of nodes with similar numbers of edges to connect. This tendency, (13, 14) identifies local connection patterns that appear more called assortativity, affects crucial structural and dynamic properties frequently in the real-world network than in ensembles of rando- of real-world networks, such as error tolerance or epidemic spread- mized networks. In this context, edge direction distinguishes ing. Here we demonstrate that edge direction has profound effects functional units like feed-forward and feedback loops. Takingedge on assortativity. We define a set of four directed assortativity mea- direction into account also overturns the simple picture of the sures and assign statistical significance by comparison to rando- World Wide Web (WWW) as having a short average distance mized networks.
    [Show full text]