COURSE OVERVIEW

 

The field of Social Network Analysis is one of the most rapidly growing fields of the social sciences. Social network analysis focuses on the relationships that exist between individuals (or other units of analysis) such as friendship, advice, trust, or trade relationships. As such, network analysis is concerned with the visualization and analysis of network structures, as well as with the importance of networks for individuals’ propensities to adopt different kinds of behaviors.

 

Up until now, researchers wishing to implement this type of analysis have been force to use specialized software for network analysis. A new set of user written commands (developed by Thomas Grund, co-author of the forthcoming Stata Press title “An Introduction to Social Network Analysis and Agent-Based Modeling Using Stata”) are however, now available for Stata.

 

This workshop introduces the so-called nwcommands suite of over 90 Stata commands for social network analysis. The suite includes commands for importing, exporting, loading, saving, handling, manipulating, replacing, generating, visualizing, and animating networks. It also includes commands for measuring various properties of the networks and the individual nodes, for detecting network patterns and measuring the similarity of different networks, as well as advanced statistical techniques for network analysis including MR-QAP and ERGM.

 

In common with TStat’s workshop philosophy, each individual session, is composed of both a theoretical component (in which the techniques and underlying principles behind them are explained), and an applied (hands-on) segment, during which participants have the opportunity to implement the techniques using real data under the watchful eye of the course tutor. Throughout the workshop, theoretical sessions are reinforced by case study examples, in which the course tutor discusses current research issues, highlighting potential pitfalls and the advantages of individual techniques. The intuition behind the choice and implementation of a specific technique is of the utmost importance. In this manner, course leaders are able to bridge the “often difficult” gap between abstract theoretical methodologies, and the practical issues one encounters when dealing with real data.

 

At the end of the course, participants are expected to be able to autonomously implement the theories and methodologies discussed during the workshop.

 

TARGET AUDIENCE 

 

The workshop provides an interdisciplinary opportunity for social scientists, mathematicians, computer scientists, ethnologists, epidemiologists, organizational theorists to acquire the necessary statistical tools required to analyse social networks in Stata.

 

COURSE REQUISITES

 

Working knowledge of Stata.

 

PROGRAM


SESSION I: INSTALLING NETWORK COMMANDS

 

Theoretical motivation
Networks and node attributes
Finding help: help
Managing variables
Return vector: return, ereturn
User written commands: adopath
Installation of nwcommands
Dialog boxes for network commands

 

SESSION II: GETTING STARTED WITH NETWORKS

 

Setting networks: nwset
Listing networks: nwds
Current network: nwcurrent
Using and saving networks: nwuse, nwsave
Importing and exporting networks: nwexport, nwimport
Dropping and keeping networks: nwdrop, nwkeep, nwclear
Network transformation: nwtoedge, nwfromedge

 

SESSION III: NETWORK VISUALIZATION

 

Schemes
Network visualization: nwplot, nwplotmatrix, nwplotjs
Animation of networks: nwmovie

 

SESSION IV: NETWORK EXAMINATION

 

Summarize networks: nwsummarize
Tabulate networks: nwtabulate
Dyads, triads: nwdyads, nwtriads
Simmelian ties: nwsimmelian
Components: nwcomponents

 

SESSION V: DISTANCE AND PATHS

 

Distance and paths: nwgeodesic, nwpath
Distance distribution
Shortest paths
Local and global bridges: nwbridge

 

SESSION VI: NEIGHBOURS AND CONTEXT

 

Network neighbours: nwneighbor
Attributes of neighbours: nwcontext
Attributes of neighbours at certain distance

 

SESSION VII: CENTRALITY AND CENTRALIZATION

 

Importance in networks
Degree centrality: nwdegree
Betweenness centrality: nwbetween
Katz centrality: nwkatz
Closeness centrality: nwcloseness
Centralization in networks

 

SESSION VIII: CHANGING NETWORKS

 

Extract tie values

Change networks: nwreplace, nwreplacemat, nwrecode
Symmetrize: nwsym

SESSION IX: CALCULATING WITH NETWORKS

 

Multiplying networks
Adding networks
Network generators: nwgen
Network expressions

 

SESSION X: NETWORK SIMULATION

 

Random networks
Lattice networks
Small-world networks
Preferential attachment networks
Homophily networks
Commands: nwrandom, nwsmall, nwhomophily, nwdyadprob, nwpref, nwring, nwlattice

 

SESSION XI: HYPOTHESIS TESTING 1

 

Correlation of networks
Conditional uniform graphs
Permutation tests: nwpermute

 

SESSION XII: REGRESSION BASED HYPOTHESIS TESTING

 

Logistic regression: logit
Dyad-level regression
Network transformation: nwtoedge, nwfromedge
Quadratic assignment procedure: nwqap
Short introduction to P2 models and their estimation in Stata

 

USEFUL TEXTS

 

Grund, T. and Hedström, P. (forthcoming) An Introduction to Social Network Analysis and Agent-Based Modeling Using Stata. Stata Press.

 

Grund, T. and Tatum, T. (forthcoming) Some Friends Matter More than Others: BMI Clustering Among Adolescents in Four European Countries. Network Science.

 

Helbing, D. and Grund, T. (2013) (eds.) Special Issue: Agent-Based Modeling and Techno-Social Systems. Advances in Complex Systems, Vol. 16, Issue 4 & 5.

 

Kron, T. and Grund, T. (2010) (eds.) Analytische Soziologie in der Diskussion. VS Verlag.

 

Journal Articles

 

Grund, T. and Morselli, C. (2017) Overlapping Crime: Stability and Specialization of Co-off ending Relationships. Social Networks. 51, 14-22

 

Grund, T. and Densley, J. (2015) Ethnic Homophily and Triad Closure: Mapping Internal Gang Structure Using Exponential Random Graph Models. Journal of Contemporary Criminal Justice, Vol. 31, Issue, 3, pp. 354-370.

 

Block, P. and Grund, T. (2014) Multidimensional Similarities in Friendship Networks. Network Science, Vol. 2, Issue 2, pp. 189-212.