Exeter Centre for Social Networks

Director

Professor Stefano Tasselli

Professor

- S.Tasselli@exeter.ac.uk Exeter

Deputy Director

Dr Cécile Emery

Senior Lecturer

+44 (0) 1392 722525 C.Emery@exeter.ac.uk Exeter

Academic staff

Professor Alexandra Gerbasi

Pro-Vice Chancellor & Executive Dean

A.Gerbasi@exeter.ac.uk Exeter

Dr Lorien Jasny

Associate Professor

L.Jasny@exeter.ac.uk Exeter

Professor Joe Labianca

Distinguished Research Professor

+1-404-428-4878 G.G.Labianca@exeter.ac.uk Exeter

Dr Jessica Methot

Distinguished Research Professor

+1 848-445-1112 J.J.Methot@exeter.ac.uk Exeter

Dr Stephen Nei

Lecturer

- S.M.Nei@exeter.ac.uk Exeter

Professor Leroy White

Associate Dean for Research & Impact

- L.White2@exeter.ac.uk Exeter

Postgraduate research students

Sam Chen

MRes student

Yijin (Sam) Chen is pursuing his MRes in Management at the Business School. He earned his MSc in Social Research from the University of Edinburgh, with an award of distinction and best overall performance. Sam is currently working on a research project examining the relationship between actors' network positions and promotion using an interpretable machine learning framework.

Zexi LI

PhD student

Zexi (Flavia) Li is a MPhil/Doctoral student in Organisation Studies/Management at the University of Exeter Business School. Her research focuses on studying formal and informal interpersonal workplace relationships from psychology perspective. Flavia’s current research uses attachment theory and social network methods to study the antecedents, characteristics, and outcomes of friendship network and advice network in the workplace.

Aaron Page

PhD student

Aaron Page is a doctoral student at the University of Exeter Business School. His research interests lie in the fields of gender and leadership, with a specific focus on gender and corporate boards. Aaron’s research highlights the antecedents and outcomes of gender diversity on corporate boards, the theories and methods employed within his work derive from the academic disciplines of social network analysis; social psychology; and institutional theory.

Laura Roldan Gomez

PhD student

Laura is a PhD student in Advanced Quantitative Methods at the University of Exeter Politics Department. Her research centres on studying the nexus between the armed conflict and the deforestation in Colombia. For her research, Laura uses a social-ecological networks approach.

INSNA/Sunbelt online workshop

The workshop is designed to provide PhD students and junior faculty with the opportunity to discuss their organizational network research in an informal and collegial setting with prominent organizational network scholars. To do so, the workshop is structured in two parts. First, a panel of senior researchers will share their experience and advice regarding developing high quality research and publishing in top journals. Second, based on their proposed research theory and methods, the junior scholars will be matched in groups of 3-4 with a more senior scholar with similar research interests. We particularly encourage junior scholars working in the following areas to attend the workshop: innovation, entrepreneurship, HRM, organizational behavior, organization and management theory, organizational change, leadership, negative ties, gender and diversity. At the session, the senior scholars will discuss research proposals submitted prior to the workshop by the junior scholars, providing insights and suggestions for improving their research. The intent is for authors to receive actionable feedback that can then be incorporated into their papers.

Workshop on Longitudinal Network Analysis with RSiena (online)

The Exeter Centre for Social Networks is offering a workshop on longitudinal social network analysis focused around the RSiena software. The workshop will be led by Professors Tom Snijders and Alessandro Lomi. The workshop is divided in two modules. Participants can register to each module of the workshop independently:

1. The first module of the workshop will take place from Monday August 24th 2020 till Wednesday August 26th 2020. It introduces participants to the analysis of longitudinal, group-centered network data by way of stochastic actor-oriented models (Snijders, van de Bunt & Steglich, 2010), and to the analysis of peer influence processes taking place in such dynamically changing networks (Steglich, Snijders & Pearson, 2010). Its objective is that participants develop an understanding of the models, familiarise themselves with the use of the RSiena package for model estimation, and learn how to tell a good model specification from a bad one. Participation in this introductory module should be sufficient preparation for following the advanced one.
2. The second module will take place on Thursday August 27th and Friday August 28th 2020. It will on the one hand address advanced topics and introduce to new developments in RSiena, such as the multilevel analysis of multi-group data with the help of random effects models instantiated in the sienaBayes function, the analysis of continuous dependent actor variables, and hints for forward model selection obtained from the sienaGOF function. On the other hand, there will be a limited opportunity to present and discuss draft papers of participants using RSiena.

For both modules, researchers who are in the process of collecting or analysing own longitudinal data sets are especially welcome to participate and, if possible, bring their own data. If participants wish to use their own data as example material, this should be communicated in advance with the teachers of the course. For participants without own data, sample data sets will be made available. For participants of the second module who would like to present a draft paper, this should be communicated with the teachers of the course before July 31. A decision about the suitability will be based on an abstract, and the draft paper should be available by August 10.

Prerequisites for participation are familiarity with basic social network analysis, knowledge of intermediate statistics (including logistic regression analysis), and familiarity with the R statistical software environment.

Tom A.B. Snijders is professor of Statistics and Methodology in the Social Sciences at the University of Groningen and emeritus fellow of Nuffield College, University of Oxford. He studied mathematics and obtained a PhD in 1979 from the University of Groningen with a dissertation in mathematical statistics. His research concentrates on social network analysis and multilevel analysis. His work on developing statistical methodology for network dynamics is implemented in the software package RSiena (Simulation Inference for Empirical Network Analysis) in the statistical system R. With Roel J. Bosker he wrote Multilevel Analysis; An Introduction to Basic and Advanced Multilevel Modeling (Sage, 2nd ed., 2012). Combining these two research strands, together with Emmanuel Lazega he edited Multilevel Network Analysis for the Social Sciences; Theory, Methods and Applications (Springer, 2016). Together with Patrick Doreian he was co-editor of Social Networks from 2006 to 2011. In 2005 he received an honorary doctorate in the social sciences at the University of Stockholm, in 2010 was the recipient of the Georg Simmel Award of INSNA, the International Network for Social Network Analysis, and in 2011 he received an honorary doctorate at the Université Paris-Dauphine.

Alessandro Lomi is a Distinguished Research Professor at the University of Exteter, and a professor at the University of Lugano (Switzerland) where he is a member of the Institute of Computational Science. He is a Senior Research Fellow in the School of Psychological Sciences of the University of Melbourne, and a Life Member of Clare Hall College, University of Cambridge. In the recent past, he was an elected member of the Swiss National Science Foundation, and a Jemolo Research Fellow at Nuffield College, University of Oxford. He holds a PhD from Cornell University (New York).

Workshop on Social Network Analysis and Text Analysis

Instructor: 

Jesse Michael Fagan, PhD

Jesse Fagan is a Lecturer of Data Analytics in the Management Department at the University of Exeter Business School. His current work focuses on the analysis of social networks and natural language processing to predict organizational behavior and outcomes. He has published work in on organizational mergers, social network analysis, and virtual worlds.

Contents and objectives:

An ocean of data is created each day in the form of digital traces - emails, tweets, discussion forums, patents, legal documents, etc. This data is a tremendous resource for testing or exploring social theories in microscopic, longitudinal detail. Attendees of this workshop will learn how to integrate text analysis and social network analysis. The workshop will focus on the different ways of extracting linguistic information from text, drawing relationships between people / organizations and documents / language. In brief at the end of the day, workshop attendees should expect to be able to know or accomplish the following:

• Basic steps in processing text information to use in analysis

• Use topic models, sentiment analysis, and linguistic behavior as node attributes

• Create visualizations that map what people talk about to who they talk to

• Create networks of documents and words and compare them

• Use text and network features to predict and model outcomes and behavior

Software resources:

• R / RStudio

Prerequisites

• A basic understanding social network analysis

• Experience using R is helpful, but not required

Bibliography

• A selection of published works on networks and language:
• Aral, S., & Van Alstyne, M. 2011. The Diversity-Bandwidth Trade-off. The American Journal of Sociology, 117(1): 90–171.
• Srivastava, S. B., Goldberg, A., & Manian, V. G. 2018. Enculturation trajectories: Language, cultural adaptation, and individual outcomes in organizations. Management Science, 64(3): 1348–1364.
• Pennebaker, J. W., Mehl, M. R., & Niederhoffer, K. G. 2003. Psychological aspects of natural language. use: our words, our selves. Annual Review of Psychology, 54: 547–577.
• Hannigan, T., Haans, R. F. J., Vakili, K., Tchalian, H., Glaser, V., et al. 2019. Topic Modeling in Management Research: Rendering New Theory from Textual Data. Academy of Management Annals, 13(2). https://doi.org/10.5465/annals.2017.0099.

Exponential Random Graph Models with Statnet

Contents and objectives:

This workshop will provide a hands-on tutorial to using exponential-family random graph models (ERGMs) for statistical analysis of social networks, using the "ergm" package in statnet. The ergm package provides tools for the specification, estimation, assessment and simulation of ERGMs that incorporate the complex dependencies within networks. Topics covered in this workshop include: an overview of the ERGM framework; defining and fitting models to empirical data; interpretation of model coefficients; goodness-of-fit and model adequacy checking; simulation of networks using ERG models; and degeneracy assessment and avoidance. Advanced topics (day 2) include more complex constraints, modelling and simulation of complete networks from egocentrically sampled data, bipartite networks, valued data, and the separable temporal ERG model. statnet is an open source collection of integrated packages for the R statistical computing environment that support the representation, manipulation, visualization, modelling, simulation, and analysis of network data.

Software:

The workshop is taught using R and RStudio and the statnet suite of packages. Please install both before coming to the course.

Prerequisites:

Participants taking this course are expected to be familiar with the basic concepts of descriptive statistics, and have an active interest in statistical inference. The basic elements of the R programming language needed to specify, estimate, and interpret network models in statnet will be introduced in the early stages of the seminar. Ideally participants will have some of their own networks to work with during some open time at the end of the workshop, but if not, the instructor can help find something of interest.

Instructor:

Dr Lorien Jasny is a Senior Lecturer in the Department of Politics at the University of Exeter and has been teaching workshops on ERG models for over 10 years.

References:

Lusher, D., Koskinen, J. and Robins, G. eds., 2013. Exponential random graph models for social networks: Theory, methods, and applications. Cambridge University Press.

Dynamic Network Actor Models (DyNAMs) using the Goldfish package in R

Workshop content and objectives

The advent of electronic communication, social media, and human sensor technologies have brought about a wealth of fine-grained social interaction data that are often easily accessible to social scientists. Archival network data sources also often come with detailed information about duration and order of relational ties. This workshop introduces and compares different approaches for the analysis of relational event data. The goal is to provide an overview of research problems that relate to relational event data, to enable participants to conduct basic analyses with the Goldfish package, and to introduce conceptual and practical differences between actor-oriented and tie-oriented network event models. 

In particular, the workshop introduces two types of Dynamic Network Actor Models (DyNAMs), one for directed event data and one for time-stamped coordination networks. Both actor-oriented models are compared to tie-oriented Relational Event Models.

Software

The workshop is taught using R and RStudio. Please install both before coming to the workshop. The latest version of goldfish is available on Github: https://github.com/snlab-​ch/goldfish.

Prerequisites

Participants taking this course are expected to be familiar with the basic concepts of descriptive statistics, and have an active interest in dynamic networks. The basic elements of the R programming language needed to specify, estimate, and interpret network models in Goldfish will be introduced in the early stages of the seminar.

Instructor

Christoph Stadtfeld is Assistant Professor of Social Networks at ETH Zürich, Switzerland. He holds a PhD from Karlsruhe Institute of Technology and has been postdoctoral researcher and Marie-​Curie fellow at the University of Groningen, the Social Network Analysis Research Center in Lugano, and the MIT Media Lab. His research focuses on the development and application of theories and methods for social network dynamics.

References

• Stadtfeld, C., Hollway, J. & Block, P.  2017. Dynamic Network Actor Models: Investigating Coordination Ties through Time. Sociological Methodology, 47(1): 1-40
• Stadtfeld, C. & Block, P. 2017. Interactions, Actors and Time: Dynamic Network Actor Models for Relational Events. Sociological Science, 4: 318-352.

The analysis of social networks: From description to statistical modelling

Contents and objectives

Data typically collected in the social sciences rely on the familiar case-by-variable research design, where "cases" (rows) represent various kinds of social actors, and "variables" (columns) contain measurements on a set of attributes of the actors or their context. Quantitative research based on this design typically emphasizes relations among the "variables." Social network research, by contrast, focuses on relations among the "cases." This change of perspective requires the development of specialized models and methods to represent, describe and analyze relational data. The course starts by introducing the basic theoretical and conceptual background of social network research, the fundamental ideas underlying the network approach, and discusses its many domains of empirical application. The course then proceeds to examine the basic analytical concepts needed to describe and understand the structure of social networks across various levels of analysis. Participants will learn how to visualize social network data to discover their main structural features, and how to implement different types of network research designs and approaches to data collection. The course also introduces contemporary statistical models for social networks, so that participants may learn how to test hypotheses using network data. Permutation tests (QAP), Exponential Random Graphs models (ERGMs) and Stochastic Actor-oriented Models (SAOMs) will be introduced as examples of statistical models for studying network structure and connective behavior. The course will include practical examples and hands-on computer laboratories based on the analysis of real-life relational data. In the laboratories, the emphasis will be on the analysis of social networks in structured social and economic settings such as, for example, business companies, and other formal organizations like hospitals, universities and other educational institutions. Students will also be given the opportunity to work with their own data and consult privately with the instructors about their own research work and problems.

Software resources

The software packages that will be introduced during the workshop include Statnet, Visone, PNet and RSIENA. The software resources used in the course are all publicly and freely available. Depending on the interests of the participants, specialized software resources developed for the R environment may also be illustrated.

Prerequisites

Participants taking this course are expected to be familiar with the basic concepts of descriptive statistics, and have an active interest in statistical inference. The basic elements of the R programming language needed to specify, estimate, and interpret network models will be introduced in the early stages of the seminar.

Instructors

Alessandro Lomi is a Distinguished Research Professor at the University of Exteter, and a professor at the University of Lugano (Switzerland) where he is a member of the Institute of Computational Science. He is a Senior Research Fellow in the School of Psychological Sciences of the University of Melbourne, and a Life Member of Clare Hall College, University of Cambridge. In the recent past, he was an elected member of the Swiss National Science Foundation, and a Jemolo Research Fellow at Nuffield College, University of Oxford. He holds a PhD from Cornell University (New York).

Viviana Amati is a postdoctoral researcher at the Social Networks Lab, ETH Zurich. She received her Ph.D. in Statistics from the University of Milano-Bicocca and has previously worked as a postdoctoral researcher at the University of Konstanz. Her primary research interest is statistical analysis and modelling of dynamic networks with a focus on estimation and misspecification of stochastic models for relational data.

Bibliography: General references

• Amati, V., Lomi, A. and Mira, A., 2018. Social network modeling. Annual Review of Statistics and Its Application, 5, pp.343-369.
• Borgatti, S.P., Mehra, A., Brass, D.J. and Labianca, G., 2009. Network analysis in the social sciences. Science, 323(5916), pp.892-895.
• Breiger, R.L. 2004.The Analysis of Social Networks. In Handbook of Data Analysis (pp. 505-526), edited by Melissa Hardy and Alan Bryman. London: SAGE Publications
• Brandes, U., Robins, G., McCranie, A., & Wasserman, S., 2013. What is network science?. Network Science, 1(1), 1-15.
• Butts, C.T., 2008. Social network analysis: A methodological introduction. Asian Journal of Social Psychology, 11(1), pp.13-41.
• Lusher, D., Koskinen, J. and Robins, G. eds., 2013. Exponential random graph models for social networks: Theory, methods, and applications. Cambridge University Press.
• Hennig, M., Brandes, U., Pfeffer, J., and Mergel, I., 2012. Studying social networks: A guide to empirical research. Campus Verlag.
• Robins, G. 2015. Doing Social Networks Research: Network Research Design for Social Scientists. Sage.
• Snijders, T.A., 2011. Statistical models for social networks. Annual review of sociology, 37, pp.131-153.
• Snijders, T.A., Van de Bunt, G.G. and Steglich, C.E., 2010. Introduction to stochastic actor-based models for network dynamics. Social networks, 32(1), pp.44-60.