Social Network Analysis Measuring, Mapping, and Modeling Collections of Connections
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Networkx: Network Analysis with Python
NetworkX: Network Analysis with Python Salvatore Scellato Full tutorial presented at the XXX SunBelt Conference “NetworkX introduction: Hacking social networks using the Python programming language” by Aric Hagberg & Drew Conway Outline 1. Introduction to NetworkX 2. Getting started with Python and NetworkX 3. Basic network analysis 4. Writing your own code 5. You are ready for your project! 1. Introduction to NetworkX. Introduction to NetworkX - network analysis Vast amounts of network data are being generated and collected • Sociology: web pages, mobile phones, social networks • Technology: Internet routers, vehicular flows, power grids How can we analyze this networks? Introduction to NetworkX - Python awesomeness Introduction to NetworkX “Python package for the creation, manipulation and study of the structure, dynamics and functions of complex networks.” • Data structures for representing many types of networks, or graphs • Nodes can be any (hashable) Python object, edges can contain arbitrary data • Flexibility ideal for representing networks found in many different fields • Easy to install on multiple platforms • Online up-to-date documentation • First public release in April 2005 Introduction to NetworkX - design requirements • Tool to study the structure and dynamics of social, biological, and infrastructure networks • Ease-of-use and rapid development in a collaborative, multidisciplinary environment • Easy to learn, easy to teach • Open-source tool base that can easily grow in a multidisciplinary environment with non-expert users -
A Network Approach of the Mandatory Influenza Vaccination Among Healthcare Workers
Wright State University CORE Scholar Master of Public Health Program Student Publications Master of Public Health Program 2014 Best Practices: A Network Approach of the Mandatory Influenza Vaccination Among Healthcare Workers Greg Attenweiler Wright State University - Main Campus Angie Thomure Wright State University - Main Campus Follow this and additional works at: https://corescholar.libraries.wright.edu/mph Part of the Influenza Virus accinesV Commons Repository Citation Attenweiler, G., & Thomure, A. (2014). Best Practices: A Network Approach of the Mandatory Influenza Vaccination Among Healthcare Workers. Wright State University, Dayton, Ohio. This Master's Culminating Experience is brought to you for free and open access by the Master of Public Health Program at CORE Scholar. It has been accepted for inclusion in Master of Public Health Program Student Publications by an authorized administrator of CORE Scholar. For more information, please contact library- [email protected]. Running Head: A NETWORK APPROACH 1 Best Practices: A network approach of the mandatory influenza vaccination among healthcare workers Greg Attenweiler Angie Thomure Wright State University A NETWORK APPROACH 2 Acknowledgements We would like to thank Michele Battle-Fisher and Nikki Rogers for donating their time and resources to help us complete our Culminating Experience. We would also like to thank Michele Battle-Fisher for creating the simulation used in our Culmination Experience. Finally we would like to thank our family and friends for all of the -
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. -
Measuring Homophily
Measuring Homophily Matteo Cristani, Diana Fogoroasi, and Claudio Tomazzoli University of Verona fmatteo.cristani, diana.fogoroasi.studenti, [email protected] Abstract. Social Network Analysis is employed widely as a means to compute the probability that a given message flows through a social net- work. This approach is mainly grounded upon the correct usage of three basic graph- theoretic measures: degree centrality, closeness centrality and betweeness centrality. We show that, in general, those indices are not adapt to foresee the flow of a given message, that depends upon indices based on the sharing of interests and the trust about depth in knowledge of a topic. We provide new definitions for measures that over- come the drawbacks of general indices discussed above, using Semantic Social Network Analysis, and show experimental results that show that with these measures we have a different understanding of a social network compared to standard measures. 1 Introduction Social Networks are considered, on the current panorama of web applications, as the principal virtual space for online communication. Therefore, it is of strong relevance for practical applications to understand how strong a member of the network is with respect to the others. Traditionally, sociological investigations have dealt with problems of defining properties of the users that can value their relevance (sometimes their impor- tance, that can be considered different, the first denoting the ability to emerge, and the second the relevance perceived by the others). Scholars have developed several measures and studied how to compute them in different types of graphs, used as models for social networks. This field of research has been named Social Network Analysis. -
Examination of the Use of Online and Offline Networks by Housing Social Movement Organizations
University of Kentucky UKnowledge Theses and Dissertations--Sociology Sociology 2013 Examination of the Use of Online and Offline Networksy b Housing Social Movement Organizations Jessica N. Kropczynski University of Kentucky, [email protected] Right click to open a feedback form in a new tab to let us know how this document benefits ou.y Recommended Citation Kropczynski, Jessica N., "Examination of the Use of Online and Offline Networksy b Housing Social Movement Organizations" (2013). Theses and Dissertations--Sociology. 11. https://uknowledge.uky.edu/sociology_etds/11 This Doctoral Dissertation is brought to you for free and open access by the Sociology at UKnowledge. It has been accepted for inclusion in Theses and Dissertations--Sociology by an authorized administrator of UKnowledge. For more information, please contact [email protected]. STUDENT AGREEMENT: I represent that my thesis or dissertation and abstract are my original work. Proper attribution has been given to all outside sources. I understand that I am solely responsible for obtaining any needed copyright permissions. I have obtained and attached hereto needed written permission statements(s) from the owner(s) of each third-party copyrighted matter to be included in my work, allowing electronic distribution (if such use is not permitted by the fair use doctrine). I hereby grant to The University of Kentucky and its agents the non-exclusive license to archive and make accessible my work in whole or in part in all forms of media, now or hereafter known. I agree that the document mentioned above may be made available immediately for worldwide access unless a preapproved embargo applies. -
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 -
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 -
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. -
Networkx Reference Release 1.9.1
NetworkX Reference Release 1.9.1 Aric Hagberg, Dan Schult, Pieter Swart September 20, 2014 CONTENTS 1 Overview 1 1.1 Who uses NetworkX?..........................................1 1.2 Goals...................................................1 1.3 The Python programming language...................................1 1.4 Free software...............................................2 1.5 History..................................................2 2 Introduction 3 2.1 NetworkX Basics.............................................3 2.2 Nodes and Edges.............................................4 3 Graph types 9 3.1 Which graph class should I use?.....................................9 3.2 Basic graph types.............................................9 4 Algorithms 127 4.1 Approximation.............................................. 127 4.2 Assortativity............................................... 132 4.3 Bipartite................................................. 141 4.4 Blockmodeling.............................................. 161 4.5 Boundary................................................. 162 4.6 Centrality................................................. 163 4.7 Chordal.................................................. 184 4.8 Clique.................................................. 187 4.9 Clustering................................................ 190 4.10 Communities............................................... 193 4.11 Components............................................... 194 4.12 Connectivity.............................................. -
Facing the Future: European Research Infrastructures for the Humanities and Social Sciences
Facing the Future: European Research Infrastructures for the Humanities and Social Sciences Adrian Duşa, Dietrich Nelle, Günter Stock, and Gert G. Wagner (Eds.) Facing the Future: European Research Infrastructures for the Humanities and Social Sciences E d i t o r s : Adrian Duşa (SCI-SWG), Dietrich Nelle (BMBF), Günter Stock (ALLEA), and Gert. G. Wagner (RatSWD) ISBN 978-3-944417-03-5 1st edition © 2014 SCIVERO Verlag, Berlin SCIVERO is a trademark of GWI Verwaltungsgesellschaft für Wissenschaftspoli- tik und Infrastrukturentwicklung Berlin UG (haftungsbeschränkt). This book documents the results of the conference Facing the Future: European Research Infrastructure for Humanities and Social Sciences (November 21/22 2013, Berlin), initiated by the Social and Cultural Innovation Strategy Work- ing Group of ESFRI (SCI-SWG) and the German Federal Ministry of Education and Research (BMBF), and hosted by the European Federation of Academies of Sciences and Humanities (ALLEA) and the German Data Forum (RatSWD). Thanks and appreciation are due to all authors, speakers and participants of the conference, and all involved institutions, in particular the German Federal Ministry of Education and Research (BMBF). The ministry funded the conference and this subsequent publication as part of the Union of the German Academies of Sciences and Humanities’ project “Survey and Analysis of Basic Humanities and Social Science Research at the Science Academies Related Research Insti- tutes of Europe”. The views expressed in this publication are exclusively the opinions of the authors and not those of the German Federal Ministry of Education and Research. Editing: Dominik Adrian, Camilla Leathem, Thomas Runge, Simon Wolff Layout and graphic design: Thomas Runge Contents Preface . -
Extracting Insights from Differences: Analyzing Node-Aligned Social
Extracting Insights from Differences: Analyzing Node-aligned Social Graphs by Srayan Datta A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Computer Science and Engineering) in The University of Michigan 2019 Doctoral Committee: Associate Professor Eytan Adar, Chair Associate Professor Mike Cafarella Assistant Professor Danai Koutra Associate Professor Clifford Lampe Srayan Datta [email protected] ORCID iD: 0000-0002-5800-830X c Srayan Datta 2019 To my family and friends ii ACKNOWLEDGEMENTS There are several people who made this dissertation possible, first among this long list is my adviser, Eytan Adar. Pursuing a doctoral program after just finishing un- dergraduate studies can be a daunting task but Eytan made it easy with his patience, kindness, and guidance. I learned a lot from our collaborations and idle conversations and I am very grateful for that. I would like to extend my thanks to the rest of my thesis committee, Mike Ca- farella, Danai Koutra and Cliff Lampe for their suggestions and constructive feed- back. I would also like to thank the following faculty members, Daniel Romero, Ceren Budak, Eric Gilbert and David Jurgens for their long insightful conversations and suggestions about some of my projects. I would like to thank all of friends and colleagues who helped (as a co-author or through critique) or supported me through this process. This is an enormous list but I am especially thankful to Chanda Phelan, Eshwar Chandrasekharan, Sam Carton, Cristina Garbacea, Shiyan Yan, Hari Subramonyam, Bikash Kanungo, and Ram Srivatasa. I would like to thank my parents for their unwavering support and faith in me. -
Analyzing Social Media Network for Students in Presidential Election 2019 with Nodexl
ANALYZING SOCIAL MEDIA NETWORK FOR STUDENTS IN PRESIDENTIAL ELECTION 2019 WITH NODEXL Irwan Dwi Arianto Doctoral Candidate of Communication Sciences, Airlangga University Corresponding Authors: [email protected] Abstract. Twitter is widely used in digital political campaigns. Twitter as a social media that is useful for building networks and even connecting political participants with the community. Indonesia will get a demographic bonus starting next year until 2030. The number of productive ages that will become a demographic bonus if not recognized correctly can be a problem. The election organizer must seize this opportunity for the benefit of voter participation. This study aims to describe the network structure of students in the 2019 presidential election. The first debate was held on January 17, 2019 as a starting point for data retrieval on Twitter social media. This study uses data sources derived from Twitter talks from 17 January 2019 to 20 August 2019 with keywords “#pilpres2019 OR #mahasiswa since: 2019-01-17”. The data obtained were analyzed by the communication network analysis method using NodeXL software. Our Analysis found that Top Influencer is @jokowi, as well as Top, Mentioned also @jokowi while Top Tweeters @okezonenews and Top Replied-To @hasmi_bakhtiar. Jokowi is incumbent running for re-election with Ma’ruf Amin (Senior Muslim Cleric) as his running mate against Prabowo Subianto (a former general) and Sandiaga Uno as his running mate (former vice governor). This shows that the more concentrated in the millennial generation in this case students are presidential candidates @jokowi. @okezonenews, the official twitter account of okezone.com (MNC Media Group).