Dynamic Social Network Analysis: Present Roots and Future Fruits
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Syllabus Organizational Network Analysis V2.2
SYLLABUS Organizational Network Analysis Self-paced online Learn to set up an organizational network analysis and create masterclass an x-ray vision into the inner workings of your organization. INDEX About the AIHR Academy Page 3 Masterclass overview Page 4 Learning objectives Page 6 Who you will learn from Page 7 What you will learn Page 8 Your success team Page 13 Frequently Asked Questions Page 14 Enroll Now Page 16 Organizational Network Analysis | Syllabus Copyright © HR Analytics Academy | Page 2 ABOUT THE AIHR ACADEMY With the HR Analytics Academy we recorded so that you can learn whenever and teach the skills that you need in order wherever it’s most convenient. By placing to succeed in the field of People you in the driver’s seat we enable you to optimize your own learning curve and Analytics. By teaching you to leverage complete each course at your own pace. the power of data, we enable you to claim the strategic impact that you Practical bite-sized lessons deserve. The practical nature of our courses and the People Analytics is about leveraging data in way they are structured is what sets us apart. order to make better informed (data-driven) Our lessons are bite-sized and conveniently people decisions. Decisions which in the end split up into several modules. Within each drive better outcomes for both the business module you will typically find a combination and employees. of 3 – 4 video lessons, a short quiz, reading materials that provide extra context, a piece Online learning portal of bonus content, and a practical assignment All of our courses and masterclasses are that will help you put your new skills into delivered through the online learning portal. -
Computational Modeling of Complex Socio-Technical Systems
Computational Modeling of Complex Socio‐Technical Systems 08‐810, 08‐621 Syllabus Fall 2016 12 Course Units Prof. Kathleen M. Carley E‐Mail: [email protected] Phone: x8‐6016 Office: Wean 5130 Office Hours: by appointment T.A.: Geoffrey Morgan Lectures: Monday, Wednesday 3:30 PM – 5:20 PM **There will be no class scheduled for Labor Day September 5th **There will be no classes scheduled on Wednesday November 23rd for the Thanksgiving Holiday **Recitation sections will be organized as needed for specific technologies – construct, system dynamics, and abms – times TBA and this area optional All course information is available on‐line via CMU Blackboard: http://www.cmu.edu/blackboard For a taste of what the course can be like: A fun little explainer and tutorial on Schelling's Dynamic Segregation Model, with a tie in for the need for awareness about diversity. http://ncase.me/polygons/ DESCRIPTION: We live and work in complex adaptive and evolving socio‐technical systems. These systems may be complex for a variety of reasons. For example, they may be complex because there is a need to coordinate many groups, because humans are interacting with technology, because there are non‐ routine or very knowledge intensive tasks. At the heart of this complexity is a set of adaptive agents who are connected or linked to other agents forming a network and who are constrained or enabled by the world they inhabit. Computational modeling can be used to help analyze, reason about, predict the behavior of, and possibly control such complex systems of "networked" agents. -
Tentative Syllabus
University of Wisconsin, Department of Sociology Sociology 375: Introduction to Mathematical Sociology Fall 2014 Prof. James Montgomery [email protected] 2436 Social Science Office hours: Friday 9:30-11:30 AM or by appointment Overview. Mathematical sociologists use mathematics to represent and analyze sociological concepts and theories. In this course, we explore mathematical models of social structure, focusing especially on social network analysis and related methods. A second course in mathematical sociology, Soc 376, which is taught in the spring, focuses on mathematical models of social process, addressing the use of Markov chains and dynamical systems in sociology. Prerequisites. There is no particular mathematics prerequisite. However, past experience suggests that students with weak backgrounds in math often find the course quite difficult. We will make extensive use of matrix algebra (as well as the matrix-algebra software package Matlab), though the course is intended to be self-contained for motivated students who have not already studied this topic. We will also learn and employ some elementary concepts from set theory and graph theory. Students who have taken Math 240 (Discrete Mathematics) and Math 340 (Matrix Algebra) or equivalents will already know the relevant mathematics. Students who have previously used mathematical software and/or have some background in computer programming may have an advantage. There is no sociology prerequisite, so the course is well- suited for students with other (quantitative) majors. Evaluation. Grades will be based on two exams (a midterm and a final), as well as problem sets. The midterm exam will be held in class on Thursday, October 23. -
Examples of Online Social Network Analysis Social Networks
Examples of online social network analysis Social networks • Huge field of research • Data: mostly small samples, surveys • Multiplexity Issue of data mining • Longitudinal data McPherson et al, Annu. Rev. Sociol. (2001) New technologies • Email networks • Cellphone call networks • Real-world interactions • Online networks/ social web NEW (large-scale) DATASETS, longitudinal data New laboratories • Social network properties – homophily – selection vs influence • Triadic closure, preferential attachment • Social balance • Dunbar number • Experiments at large scale... 4 Another social science lab: crowdsourcing, e.g. Amazon Mechanical Turk Text http://experimentalturk.wordpress.com/ New laboratories Caveats: • online links can differ from real social links • population sampling biases? • “big” data does not automatically mean “good” data 7 The social web • social networking sites • blogs + comments + aggregators • community-edited news sites, participatory journalism • content-sharing sites • discussion forums, newsgroups • wikis, Wikipedia • services that allow sharing of bookmarks/favorites • ...and mashups of the above services An example: Dunbar number on twitter Fraction of reciprocated connections as a function of in- degree Gonçalves et al, PLoS One 6, e22656 (2011) Sharing and annotating Examples: • Flickr: sharing of photos • Last.fm: music • aNobii: books • Del.icio.us: social bookmarking • Bibsonomy: publications and bookmarks • … •“Social” networks •“specialized” content-sharing sites •Users expose profiles (content) and links -
Learning Within and Among Organizations
LEARNING WITHIN AND AMONG ORGANIZATIONS Kathleen M. Carley Social and Decision Sciences and H. J. Heinz III School of Policy and Management Carnegie Mellon University ACKNOWLEDGMENT This work was supported in part by the Office of Naval Research (ONR), United States Navy Grant No. N00014-97-1-0037 and by the National Science Foundation under Grant No. IRI9633 662. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Office of Naval Research, the National Science Foundation, or the U.S. government. Reference: Kathleen M. Carley, 1999, “Learning Within and Among Organizations.” Ch. 1 in Philip Anderson, Joel Baum and Anne Miner (Eds.) Advances in Strategic Management: Population-Level Learning and Industry Change, Vol. 16. Elsevier Science Ltd. Pp. 33-56. - 1 - ABSTRACT Change is readily seen both within organizations and within populations of organizations. Such change has been characterized as organization or population level learning or evolution. Underlying such change, is change at the individual human and social network level. Herein, it is asked, how does the way in which individuals learn and the way in which networks evolve reflect itself in organizational and population level learning. What changes should emerge at the organization and population level due to learning, information diffusion, and network change at the individual level? Herein it is argued that organization and population level phenomena, such as performance improvements, mis-learning, and shakeouts emerge from the on-going processes of change at the individual level. Looking at learning and information diffusion enables the organizational theorist to link micro and macro level organizational phenomena. -
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. -
Mark Sheldon Mizruchi
August 2014 Mark Sheldon Mizruchi Office Address: Department of Sociology Phone: (734) 764-7444 University of Michigan FAX: (734) 763-6887 Ann Arbor, Michigan 48109-1382 Email: [email protected] Homepage: http://www-personal.umich.edu/~mizruchi/ Education: 1980, Ph.D., State University of New York at Stony Brook (sociology) 1977, M.A., State University of New York at Stony Brook (sociology) 1975, A.B., Washington University, St. Louis (sociology) Positions Held (post-Ph.D.): 2014 (September)-: Robert Cooley Angell Collegiate Professor of Sociology, University of Michigan 2012-: Barger Family Professor of Organizational Studies, University of Michigan 2012-: Director, Organizational Studies Program, University of Michigan 1991-: Professor of Sociology and Business Administration, University of Michigan 1989-91: Associate Professor of Sociology, Columbia University 1987-89: Assistant Professor of Sociology, Columbia University 1983-87: Supervisor of Statistical Services, Scientific Computing Center, Albert Einstein College of Medicine 1981-87: Assistant Professor of Psychiatry (Biostatistics), Albert Einstein College of Medicine 1980-83: Statistical Analyst, Scientific Computing Center, Albert Einstein College of Medicine Areas of Interest and Expertise: 2 General: Organizational Theory, Economic Sociology, Social Network Analysis, Political Sociology Specific: Corporate Political Behavior, Social Determinants of Corporate Financing, Corporate Boards and Governance, Network Methods, Uncertainty and Ambiguity in Bank Decision Making Teaching Experience (Courses Taught): Michigan (1991-): Graduate: Statistical Methods, I and II; Economic Sociology; Theories and Practices of Sociology II (contemporary sociological theory); Structural Sociology (social network theory); Political Sociology; Research Workshop in Economic Sociology. Undergraduate: Formal Organizations and their Environments; Economic Sociology; Sociological Theory; Social Stratification; Organizations, Industries, and the State; Seminar in Structural Sociology; Seminar on Network Analysis. -
Intra-Organizational Complexity and Computation
Intra-organizational Complexity and Computation Kathleen M. Carley Social and Decision Sciences H. J. Heinz III School of Policy and Management Engineering and Public Policy Carnegie Mellon University December 2000 Running Head: Intra-organizational Complexity and Computation Direct all correspondence to: Prof. Kathleen M. Carley Dept. of Social and Decision Sciences Carnegie Mellon University Pittsburgh, PA 15213 Email: [email protected] Tel: 1-412-268-3225 Fax: 1-412-268-6938 This work was supported in part by the Office of Naval Research (ONR), United States Navy Grant No. N00014-97-1-0037 and by the National Science Foundation under Grant No. IRI9633 662. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of the Office of Naval Research, the National Science Foundation, or the U.S. government. The author thanks the following people for their comments on this and related works: Carter Butts, Ju-Sung Lee, Bill McKelvey, Benoit Morel and Ranga Ramanujam. To be published in Companion to Organizations, Joel C. Baum (Ed.), Oxford UK: Blackwell, July 2001. Intra-organizational Complexity and Computation Organizations are complex systems. They are also information processing systems comprised of a large number of agents such as human beings. Combining these perspectives and recognizing the essential non-linear dynamics that are at work leads to the standard non-linear multi-agent system conclusions such as: history matters, organizational behavior and form is path dependent, complex behavior emerges from individual interaction, and change is inevitable. -
Dynamic Network Analysis (Spring 2020) Course 17-801, 17‐685, 19‐640 Instructor Prof
Dynamic Network Analysis (Spring 2020) Course 17-801, 17-685, 19-640 Instructor Prof. Kathleen M Carley Phone: x86016 Office: Wean 5130, ISR E‐Mail: [email protected] Office Hours: by appointment; contact Sienna Watkins for scheduling [email protected] Teaching Assistant Mihovil Bartulovic, [email protected], Wean 4211 Introduction Who knows who? Who knows what? Who communicates with whom? Who is influential? How do ideas, diseases, and technologies propagate through groups? How do social media, social, knowledge, and technology networks differ? How do these networks evolve? How do network constrain and enable behavior? How can a network be compromised or made resilient? What are network cascades? Such questions can be addressed using Network Science. Network Science, a.k.a. social network analysis, link analysis, geo‐network analysis, and dynamic network analysis is a fast‐growing interdisciplinary field aimed at understanding simple & high dimensional networks, from both a static and a dynamic perspective. Across an unlimited application space, graph theoretic, statistical, & simulation methodologies are used to reason about complex systems as networks. An interdisciplinary perspective on network science is provided, with an emphasis on high‐dimensional dynamic data. The fundamentals of network science, methods, theories, metrics & confidence estimation, constraints on data collection & bias, and key research findings & challenges are examined. Illustrative networks discussed include social media based (e.g., twitter), disaster response, organizational, semantic, political elite, crises, terror, & P2P networks. Critical procedures covered include: basic centralities and metrics, group and community detection, link inference, network change detection, comparative analytics, and big data techniques. Applications from business, science, art, medicine, forensics, social media & numerous other areas are explored. -
Basics of Social Network Analysis Distribute Or
1 Basics of Social Network Analysis distribute or post, copy, not Do Copyright ©2017 by SAGE Publications, Inc. This work may not be reproduced or distributed in any form or by any means without express written permission of the publisher. Chapter 1 Basics of Social Network Analysis 3 Learning Objectives zz Describe basic concepts in social network analysis (SNA) such as nodes, actors, and ties or relations zz Identify different types of social networks, such as directed or undirected, binary or valued, and bipartite or one-mode zz Assess research designs in social network research, and distinguish sampling units, relational forms and contents, and levels of analysis zz Identify network actors at different levels of analysis (e.g., individuals or aggregate units) when reading social network literature zz Describe bipartite networks, know when to use them, and what their advan- tages are zz Explain the three theoretical assumptions that undergird social networkdistribute studies zz Discuss problems of causality in social network analysis, and suggest methods to establish causality in network studies or 1.1 Introduction The term “social network” entered everyday language with the advent of the Internet. As a result, most people will connect the term with the Internet and social media platforms, but it has in fact a much broaderpost, application, as we will see shortly. Still, pictures like Figure 1.1 are what most people will think of when they hear the word “social network”: thousands of points connected to each other. In this particular case, the points represent political blogs in the United States (grey ones are Republican, and dark grey ones are Democrat), the ties indicating hyperlinks between them. -
Soc 6460: Economic Sociology
Cornell University • Spring 2019 Syllabus Soc 6460: Economic Sociology Filiz Garip Department of Sociology 348 Uris Hall [email protected] Time: Thursday 2-4pm Location: Uris Hall 340 Office Hours: Thursday 4-5pm (Uris Hall 348) Website: search for Soc 6460 in Blackboard (www.blackboard.cornell.edu) COURSE DESCRIPTION AND OBJECTIVES This course is an introduction to the sociological examination of economic phenomena. As a subfield that has grown rapidly over the past twenty years, economic sociology has focused on three major activities: First, it has examined the prerequisites for and constraints to economic processes as defined by economists. Second, it has extended economic models to social phenomena rarely considered in the domain of economics. Third, and most ambitiously, it has tried to search for alternative accounts of phenomena typically formulated only in economic terms. This course will provide an overview of these broad concerns and approaches in economic sociology, and review the sociological explanations of economic activities of production, consumption and distribution in a wide range of settings. REQUIREMENTS Students are expected to attend each meeting, do the readings thoroughly and in advance, and participate actively in class. Emphasis is on mastering, responding critically and creatively to, and integrating the course material, with an eye toward developing your own research questions and interests. You should be able to answer the following questions about each assigned reading: • What research question is the author -
Formalist and Relationalist Theory in Social Network Analysis
Formalist and Relationalist Theory in Social Network Analysis Emily Erikson Yale University 17 March 2011 Draft Acknowledgements: I would like to thank Peter Bearman, Scott Boorman, Richard Lachmann, David Stark, Nicholas Wilson, the participants of Boston University’s Society, Politics & Culture Workshop, and Yale University’s Comparative Research Workshop for their helpful comments. s Abstract: There is a widespread understanding that social networks are relationalist. In this paper, I suggest an alternative view that relationalism is only one theoretical perspective in network analysis. Relationalism, as currently defined, rejects essentialism, a priori categories, and insists upon the intersubjectivity of experience and meaning, as well as the importance of the content of interactions and their historical setting. Formalism is based on a structuralist interpretation of the theoretical works of Georg Simmel. Simmel based his theory on a Neo-Kantian program of identifying a priori categories of relational types and patterns that operate independently of cultural content or historical setting. Formalism and relationalism are therefore entirely distinct from each other. Yet both are internally consistent theoretical perspectives. The contrast between the two plays out in their approaches to culture, meaning, agency, and generalizability. In this paper, I distinguish the two theoretical strains. 2 Since its inception in the 1930s, social network research has become an increasingly vibrant part of sociology inquiry. The field has grown tremendously over the last few decades: new journals and conferences have been created, programs and concentrations in social network analysis have been created in institutions in both North America and Europe, and large numbers of scholars have been attracted to the field from across a wide disciplinary array, including sociology, anthropology, management sciences, computer science, biology, mathematics, and physics.