Theories and methods for understanding human social
networks
Associate Professor Garry Robins & Prof Pip Pattison (University of Melbourne)
University of Adelaide, 2-6 May 2005
Day 1: Introduction, data and centrality
Session 1
0930 -1000: Coffee and registration
1000 -1100: Introduction
to social networks (Borgatti & Foster, 2003; Padgett & Ansell,
1993)
1100 -1115: Break
Session 2
1115 -1300: Exercise: Complete network data collection
Methods: What is a social network? Representing
a network
Exercise: Egocentric network data collection
Methods: Social network data collection techniques
Exercise: Network visualisation using Pajek
1300 - 1400: Lunch
Session 3
1400 - 1500: Introduction to essential graph theory
concepts (Freeman, 1979)
1500 - 1515: Break
Session 4
1515 – 1630: Exercise: UCINET: Calculating density,
components, geodesics, and centrality;
visualising social networks with Netdraw
1630 - 1730: Question time
Day 2 Structure
Session 1
0930 -1000: Review Day 1
1000 -1100: Structures:
Balance, structural holes and the strength of weak ties (Burt, Janotta
& Mahoney, 1998; Granovetter, 1973)
1100 -1130: Break
Session 2
1130 -1230: Methods:
Some definitions of cohesive subsets
Exercise: UCINET: Cohesive subsets
1230 -1330: Lunch
Session 3
1330 -1430: Conceptualisations
of network position (White, Boorman & Breiger, 1976)
1430 -1500: Break
Session 4
1500 -1545: Methods: Algorithms for network position
1545 -1630: Exercise: UCINET: structural equivalence
and blockmodels
1630 -1730: Question time
Day 3: Models for graphs and networks
Session 1
0930 - 1000: Review Day 2
1000 - 1100: Statistical
models: an introduction to random graph distributions
1100 - 1130: Break
Session 2
1130 - 1200: Stochastic
blockmodels
1200 - 1230: Exercise: Stochastic blockmodels: introducing
StOCNET
1230 - 1330: Lunch
Session 3
1330 - 1430: Small worlds:
regularity and randomness
1430 - 1500: Break
Session 4
1500 - 1545: Exercise: Comparing global structures
1545 - 1630: A brief
discussion of scale-free degree distributions and of the preferential attachment
model
1630 - 1730: Question time
Day 4: Statistical models for social networks: exponential random
graph models
Session 1
0930 - 1000: Review Day 3
1000 - 1100: Markov random
graph models (Robins, Pattison, Kalish & Lusher, 2005)
(including MCMC ML
estimation)
1100 - 1130: Break
Session 2
1130 - 1200: Simulating Markov
random graphs (Robins, Pattison & Woolcock, 2005)
1200 - 1230: Exercise: Simulating Markov random graph
distributions
1230 - 1330: Lunch
Session 3
1330 - 1430: Exponential
random graph models: New specifications (Robins, Snijders,
Wang, Handcock & Pattison, 2005)
1430 - 1500: Break
Session 4
1500 – 1545: Exercise: Fitting exponential random graph
models with pnet
1545 – 1630: Modelling
multiple networks (Lazega & Pattison, 1999)
1630 - 1700: Question time
Day 5: Complex network dependencies: Selection & influence models
Session 1
0900 - 0930: Review Day 4
0930 - 1030: Selection
and influence; Exponential random graph models for social selection (Robins
& Pattison, 2005)
1030 - 1100: Break
Session 2
1100 – 1200: Exercise: Fitting social selection models
1200 – 1230: Lunch
Session 3
1230 – 1330: Methods: Network
evolution and the co-evolution of networks and behaviour (Snijders, 2001)
1330 – 1345: Break
Session 4
1345 – 1430: Bipartite graphs
and affiliation networks
Conclusions.
Reading list
Borgatti, S., & Foster, P. (2003). The network paradigm in organizational
research: A review and typology. Journal of Management, 29, 991-1013.
Burt, R. S., Jannotta, J. E. J., & Mahoney, J. T. (1998). Personality
correlates of structural holes. Social Networks, 20, 63-87.
Contractor, N., Wasserman, S. & Faust, K. (In press). Testing multi-theoretical
multilevel hypotheses about organizational networks: An analytic framework
and empirical example. Academy of Management Journal.
Freeman, L.C. (1979). Centrality in social networks: Conceptual clarification.
Social Networks, 1, 215-239.
Granovetter, M. S. (1973). The strength of weak ties. American Journal of
Sociology 78, 1360-1380.
Lazega, E.,& Pattison, P. (1999). Multiplexity, generalized exchange
and cooperation in organizations. Social Networks, 21, 67-90.
Nowicki, Krzysztof, & Snijders, T.A.B. (2001). Estimation and prediction
for stochastic blockstructures. Journal of the American Statistical
Association, 96: 1077-1087.
Padgett, J.F., & Ansell, C.K. (1993). Robust action and the rise of the
Medici, 1400-1434. American Journal of Sociology, 98, 1259-1319.
Robins, G. L., & Pattison, P. E. (2005). Interdependencies and
social processes: dependence graphs and generalized dependence structures.
In P. Carrington, J. Scott, & S. Wasserman (Eds.), Models and methods
in social network analysis. New York: Cambridge University Press.
Robins, G.L., Pattison, P., Kalish, Y., & Lusher, D. (2005). A workshop
on exponential random graph (p*) models for social networks. Social Networks
working paper No 1/05: Psychology Department, University of Melbourne.
Robins, G.L., Pattison, P., & Woolcock, J. (2005). Small and other worlds:
Global network structures from local processes. American Journal of Sociology.
Robins, G.L., Snijders, T.A.B., & Wang, P. (2005). Recent developments
in exponential random graph (p*) models for social networks. Social networks
working paper No 2/05: Psychology Department, University of Melbourne.
Shah, P.P. (1998). Who are employees' social referents: Using a network perspective
to determine referent others. Academy of Management Journal, 41, 249-268.
Snijders, T.A.B. (2001). The statistical evaluation of social network dynamics.
In M.E. Sobel and M.P. Becker (eds.), Sociological Methodology-2001, 361-395.
Boston and London: Basil Blackwell.
Wang, P., Robins, G. & Pattison, P. (2005). Pnet user manual. University
of Melbourne.
Watts, D., & Strogatz, S. (1998). Collective dynamics of ‘small-world’
networks. Nature, 393, 440-442.
White, H.C., Boorman, S.A., & Breiger, R.L. (1976). Social structure
from multiple networks: I. Blockmodels of roles and positions. American Journal
of Sociology, 87, 517-547.