Social Network Analysis: a New Tool for Understanding Individual and Family Functioning
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Creating & Connecting: Research and Guidelines on Online Social
CREATING & CONNECTING//Research and Guidelines on Online Social — and Educational — Networking NATIONAL SCHOOL BOARDS ASSOCIATION CONTENTS Creating & Connecting//The Positives . Page 1 Online social networking Creating & Connecting//The Gaps . Page 4 is now so deeply embedded in the lifestyles of tweens and teens that Creating & Connecting//Expectations it rivals television for their atten- and Interests . Page 7 tion, according to a new study Striking a Balance//Guidance and Recommendations from Grunwald Associates LLC for School Board Members . Page 8 conducted in cooperation with the National School Boards Association. Nine- to 17-year-olds report spending almost as much time About the Study using social networking services This study was made possible with generous support and Web sites as they spend from Microsoft, News Corporation and Verizon. watching television. Among teens, The study was comprised of three surveys: an that amounts to about 9 hours a online survey of 1,277 nine- to 17-year-old students, an online survey of 1,039 parents and telephone inter- week on social networking activi- views with 250 school district leaders who make deci- ties, compared to about 10 hours sions on Internet policy. Grunwald Associates LLC, an a week watching TV. independent research and consulting firm that has conducted highly respected surveys on educator and Students are hardly passive family technology use since 1995, formulated and couch potatoes online. Beyond directed the study. Hypothesis Group managed the basic communications, many stu- field research. Tom de Boor and Li Kramer Halpern of dents engage in highly creative Grunwald Associates LLC provided guidance through- out the study and led the analysis. -
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 -
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 -
A Centrality Measure for Electrical Networks
Carnegie Mellon Electricity Industry Center Working Paper CEIC-07 www.cmu.edu/electricity 1 A Centrality Measure for Electrical Networks Paul Hines and Seth Blumsack types of failures. Many classifications of network structures Abstract—We derive a measure of “electrical centrality” for have been studied in the field of complex systems, statistical AC power networks, which describes the structure of the mechanics, and social networking [5,6], as shown in Figure 2, network as a function of its electrical topology rather than its but the two most fruitful and relevant have been the random physical topology. We compare our centrality measure to network model of Erdös and Renyi [7] and the “small world” conventional measures of network structure using the IEEE 300- bus network. We find that when measured electrically, power model inspired by the analyses in [8] and [9]. In the random networks appear to have a scale-free network structure. Thus, network model, nodes and edges are connected randomly. The unlike previous studies of the structure of power grids, we find small-world network is defined largely by relatively short that power networks have a number of highly-connected “hub” average path lengths between node pairs, even for very large buses. This result, and the structure of power networks in networks. One particularly important class of small-world general, is likely to have important implications for the reliability networks is the so-called “scale-free” network [10, 11], which and security of power networks. is characterized by a more heterogeneous connectivity. In a Index Terms—Scale-Free Networks, Connectivity, Cascading scale-free network, most nodes are connected to only a few Failures, Network Structure others, but a few nodes (known as hubs) are highly connected to the rest of the network. -
Opportunities and Challenges for Standardization in Mobile Social Networks
Opportunities and Challenges for Standardization in Mobile Social Networks Laurent-Walter Goix, Telecom Italia [email protected] Bryan Sullivan, AT&T [email protected] Abstract This paper describes the opportunities and challenges related to the standardization of interoperable “Mobile Social Networks”. Challenges addressed include the effect of social networks on resource usage, the need for social network federation, and the needs for a standards context. The concept of Mobile Federated Social Networks as defined in the OMA SNEW specification is introduced as an approach to some of these challenges. Further specific needs and opportunities in standards and developer support for mobile social apps are described, including potentially further work in support of regulatory requirements. Finally, we conclude that a common standard is needed for making mobile social networks interoperable, while addressing privacy concerns from users & institutions as well as the differentiations of service providers. 1 INTRODUCTION Online Social Networks (OSN) are dominated by Walled Gardens that have attracted users by offering new paradigms of communication / content exchange that better fit their modern lifestyle. Issues are emerging related to data ownership, privacy and identity management and some institutions such as the European Commission have started to provide measures for controlling this. The impressive access to OSN from ever smarter mobile devices, as well as the growth of mobile- specific SN services (e.g. WhatsApp) have further stimulated the mobile industry that is already starving for new attractive services (RCS 1). In this context OMA 2 as mobile industry forum has recently promoted the SNEW specifications that can leverage network services such as user identity and native interoperability of mobile networks (the approach promoted by “federated social networks”). -
Exploring Network Structure, Dynamics, and Function Using Networkx
Proceedings of the 7th Python in Science Conference (SciPy 2008) Exploring Network Structure, Dynamics, and Function using NetworkX Aric A. Hagberg ([email protected])– Los Alamos National Laboratory, Los Alamos, New Mexico USA Daniel A. Schult ([email protected])– Colgate University, Hamilton, NY USA Pieter J. Swart ([email protected])– Los Alamos National Laboratory, Los Alamos, New Mexico USA NetworkX is a Python language package for explo- and algorithms, to rapidly test new hypotheses and ration and analysis of networks and network algo- models, and to teach the theory of networks. rithms. The core package provides data structures The structure of a network, or graph, is encoded in the for representing many types of networks, or graphs, edges (connections, links, ties, arcs, bonds) between including simple graphs, directed graphs, and graphs nodes (vertices, sites, actors). NetworkX provides ba- with parallel edges and self-loops. The nodes in Net- sic network data structures for the representation of workX graphs can be any (hashable) Python object simple graphs, directed graphs, and graphs with self- and edges can contain arbitrary data; this flexibil- loops and parallel edges. It allows (almost) arbitrary ity makes NetworkX ideal for representing networks objects as nodes and can associate arbitrary objects to found in many different scientific fields. edges. This is a powerful advantage; the network struc- In addition to the basic data structures many graph ture can be integrated with custom objects and data algorithms are implemented for calculating network structures, complementing any pre-existing code and properties and structure measures: shortest paths, allowing network analysis in any application setting betweenness centrality, clustering, and degree dis- without significant software development. -
Network Biology. Applications in Medicine and Biotechnology [Verkkobiologia
Dissertation VTT PUBLICATIONS 774 Erno Lindfors Network Biology Applications in medicine and biotechnology VTT PUBLICATIONS 774 Network Biology Applications in medicine and biotechnology Erno Lindfors Department of Biomedical Engineering and Computational Science Doctoral dissertation for the degree of Doctor of Science in Technology to be presented with due permission of the Aalto Doctoral Programme in Science, The Aalto University School of Science and Technology, for public examination and debate in Auditorium Y124 at Aalto University (E-hall, Otakaari 1, Espoo, Finland) on the 4th of November, 2011 at 12 noon. ISBN 978-951-38-7758-3 (soft back ed.) ISSN 1235-0621 (soft back ed.) ISBN 978-951-38-7759-0 (URL: http://www.vtt.fi/publications/index.jsp) ISSN 1455-0849 (URL: http://www.vtt.fi/publications/index.jsp) Copyright © VTT 2011 JULKAISIJA – UTGIVARE – PUBLISHER VTT, Vuorimiehentie 5, PL 1000, 02044 VTT puh. vaihde 020 722 111, faksi 020 722 4374 VTT, Bergsmansvägen 5, PB 1000, 02044 VTT tel. växel 020 722 111, fax 020 722 4374 VTT Technical Research Centre of Finland, Vuorimiehentie 5, P.O. Box 1000, FI-02044 VTT, Finland phone internat. +358 20 722 111, fax + 358 20 722 4374 Technical editing Marika Leppilahti Kopijyvä Oy, Kuopio 2011 Erno Lindfors. Network Biology. Applications in medicine and biotechnology [Verkkobiologia. Lääke- tieteellisiä ja bioteknisiä sovelluksia]. Espoo 2011. VTT Publications 774. 81 p. + app. 100 p. Keywords network biology, s ystems b iology, biological d ata visualization, t ype 1 di abetes, oxida- tive stress, graph theory, network topology, ubiquitous complex network properties Abstract The concept of systems biology emerged over the last decade in order to address advances in experimental techniques. -
Chazen Society Fellow Interest Paper Orkut V. Facebook: the Battle for Brazil
Chazen Society Fellow Interest Paper Orkut v. Facebook: The Battle for Brazil LAUREN FRASCA MBA ’10 When it comes to stereotypes about Brazilians – that they are a fun-loving people who love to dance samba, wear tiny bathing suits, and raise their pro soccer players to the levels of demi-gods – only one, the idea that they hold human connection in high esteem, seems to be born out by concrete data. Brazilians are among the savviest social networkers in the world, by almost all engagement measures. Nearly 80 percent of Internet users in Brazil (a group itself expected to grow by almost 50 percent over the next three years1) are engaged in social networking – a global high. And these users are highly active, logging an average of 6.3 hours on social networks and 1,220 page views per month per Internet user – a rate second only to Russia, and almost double the worldwide average of 3.7 hours.2 It is precisely this broad, highly engaged audience that makes Brazil the hotly contested ground it is today, with the dominant social networking Web site, Google’s Orkut, facing stiff competition from Facebook, the leading aggregate Web site worldwide. Social Network Services Though social networking Web sites would appear to be tools born of the 21st century, they have existed since even the earliest days of Internet-enabled home computing. Starting with bulletin board services in the early 1980s (accessed over a phone line with a modem), users and creators of these Web sites grew increasingly sophisticated, launching communities such as The WELL (1985), Geocities (1994), and Tripod (1995). -
2010 Nonprofit Social Network Benchmark Report
April 2010 Nonprofit Social Network Benchmark Report www.nonprofitsocialnetworksurvey.com www.nten.org www.commonknow.com www.theport.com Introduction NTEN, Common Knowledge, and ThePort Network offer this second annual installment of the Nonprofit Social Networking Benchmark Report. This report’s objective is to provide nonprofits with insights and trends surrounding social networking technology as part of nonprofit organizations’ marketing, communications, fundraising, and program house social network services. Social networking community built on Between February 3 and March 15, 2010, a nonprofit’s own 1,173 nonprofit professionals responded to website. Term derived a survey about their organization’s use of from direct mail online social networks. house lists. Two groups of questions were posed to survey participants: commercial 1. Tells us about your use of commercial social network social networks such as Facebook, Twitter, An online community LinkedIn, and others. owned and operated by a corporation. 2. Tell us about your work building and Popular examples using social networks on your own include Facebook and websites, called house social networks . MySpace. Survey respondents represented small, medium and large nonprofits and all nonprofit segments: Arts & Culture, Association, Education, Environment & Animals, Health & Healthcare, Human Services, International, Public & Societal Benefit, Religious and others (See Appendix A for more details). www.nonprofitsocialnetworksurvey.com 1 Executive Summary Commercial Social Networks Nonprofits continued to increase their use of commercial social networks over 2009 and early 2010 with Facebook and Twitter proving to be the preferred networks. LinkedIn and YouTube held steady, but MySpace lost significant ground. The following are the key excerpts from this section: • Facebook is still used by more nonprofits than any other commercial social network with 86% of nonprofits indicating that they have a presence on this network. -
Network Analysis with Nodexl
Social data: Advanced Methods – Social (Media) Network Analysis with NodeXL A project from the Social Media Research Foundation: http://www.smrfoundation.org About Me Introductions Marc A. Smith Chief Social Scientist Connected Action Consulting Group [email protected] http://www.connectedaction.net http://www.codeplex.com/nodexl http://www.twitter.com/marc_smith http://delicious.com/marc_smith/Paper http://www.flickr.com/photos/marc_smith http://www.facebook.com/marc.smith.sociologist http://www.linkedin.com/in/marcasmith http://www.slideshare.net/Marc_A_Smith http://www.smrfoundation.org http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/ http://www.flickr.com/photos/amycgx/3119640267/ Collaboration networks are social networks SNA 101 • Node A – “actor” on which relationships act; 1-mode versus 2-mode networks • Edge B – Relationship connecting nodes; can be directional C • Cohesive Sub-Group – Well-connected group; clique; cluster A B D E • Key Metrics – Centrality (group or individual measure) D • Number of direct connections that individuals have with others in the group (usually look at incoming connections only) E • Measure at the individual node or group level – Cohesion (group measure) • Ease with which a network can connect • Aggregate measure of shortest path between each node pair at network level reflects average distance – Density (group measure) • Robustness of the network • Number of connections that exist in the group out of 100% possible – Betweenness (individual measure) F G • -
Social Networks Influence Analysis
University of North Florida UNF Digital Commons UNF Graduate Theses and Dissertations Student Scholarship 2017 Social Networks Influence Analysis Doaa Gamal University of North Florida, [email protected] Follow this and additional works at: https://digitalcommons.unf.edu/etd Part of the Computer Sciences Commons Suggested Citation Gamal, Doaa, "Social Networks Influence Analysis" (2017). UNF Graduate Theses and Dissertations. 723. https://digitalcommons.unf.edu/etd/723 This Master's Thesis is brought to you for free and open access by the Student Scholarship at UNF Digital Commons. It has been accepted for inclusion in UNF Graduate Theses and Dissertations by an authorized administrator of UNF Digital Commons. For more information, please contact Digital Projects. © 2017 All Rights Reserved SOCIAL NETWORKS INFLUENCE ANALYSIS by Doaa H. Gamal A thesis submitted to the School of Computing in partial fulfillment of the requirements for the degree of Master of Science in Computing and Information Sciences UNIVERSITY OF NORTH FLORIDA SCHOOL OF COMPUTING Spring, 2017 Copyright (©) 2017 by Doaa H. Gamal All rights reserved. Reproduction in whole or in part in any form requires the prior written permission of Doaa H. Gamal or designated representative. ii This thesis titled “Social Networks Influence Analysis” submitted by Doaa H. Gamal in partial fulfillment of the requirements for the degree of Master of Science in Computing and Information Sciences has been Approved by the thesis committee: Date Dr. Karthikeyan Umapathy Thesis Advisor and Committee Chairperson Dr. Lakshmi Goel Dr. Sandeep Reddivari Accepted for the School of Computing: Dr. Sherif A. Elfayoumy Director of the School Accepted for the College of Computing, Engineering, and Construction: Dr. -
A Sharp Pagerank Algorithm with Applications to Edge Ranking and Graph Sparsification
A sharp PageRank algorithm with applications to edge ranking and graph sparsification Fan Chung? and Wenbo Zhao University of California, San Diego La Jolla, CA 92093 ffan,[email protected] Abstract. We give an improved algorithm for computing personalized PageRank vectors with tight error bounds which can be as small as O(n−k) for any fixed positive integer k. The improved PageRank algorithm is crucial for computing a quantitative ranking for edges in a given graph. We will use the edge ranking to examine two interrelated problems — graph sparsification and graph partitioning. We can combine the graph sparsification and the partitioning algorithms using PageRank vectors to derive an improved partitioning algorithm. 1 Introduction PageRank, which was first introduced by Brin and Page [11], is at the heart of Google’s web searching algorithms. Originally, PageRank was defined for the Web graph (which has all webpages as vertices and hy- perlinks as edges). For any given graph, PageRank is well-defined and can be used for capturing quantitative correlations between pairs of vertices as well as pairs of subsets of vertices. In addition, PageRank vectors can be efficiently computed and approximated (see [3, 4, 10, 22, 26]). The running time of the approximation algorithm in [3] for computing a PageRank vector within an error bound of is basically O(1/)). For the problems that we will examine in this paper, it is quite crucial to have a sharper error bound for PageRank. In Section 2, we will give an improved approximation algorithm with running time O(m log(1/)) to compute PageRank vectors within an error bound of .