Social network analysis: A methodological introduction

Asian Journal of Social Psychology

Volumn 11, Issue 1, 2008, Pages 13-41

  Butts, Carter T ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Relational data; Social network analysis; Social structure

Indexed keywords

EID: 40249107687     PISSN: 13672223     EISSN: 1467839X     Source Type: Journal    
DOI: 10.1111/j.1467-839X.2007.00241.x     Document Type: Article

References (150)

1. Anderson, B. S., Butts, C. T. Carley, K. M. (1999). The interaction of size and density with graph-level indices. Social Networks, 21 (3 239 267.
2. Anselin, L. (1988). Spatial Econometrics: Methods and Models. Norwell, MA : Kluwer.
3. Baltz, A. Kloemann, L. (2005). Spectral analysis. In : U. Brandes T. Erlebach, eds. Network Analysis: Methodological Foundations, pp. 373 416. Berlin : Springer-Verlag.
4. Banks, D. Carley, K. M. (1994). Metric inference for social networks. Journal of Classification, 11 (1 121 149.
5. Barabási, A.-L. Albert, R. (1999). Emergence of scaling in random networks. Science, 206, 509 512.
6. Batagelj, V. Mrvar, A. (2007). Pajek - program for large network analysis. Ljubljana : Vlado Networks. Electronic data file. Available from http://vlado.fmf.uni-lj.si/pub/networks/pajek/.
7. Batchelder, W. H. Romney, A. K. (1988). Test theory without an answer key. Psychometrika, 53 (1 71 92.
8. Bavelas, A. Barrett, D. (1951). An experimental approach to organizational communication. Personnel, 27, 366 371.
9. Berge, C. (1962). The Theory of Graphs. London : Methuen and Company.
10. Bernard, H. R. Killworth, P. (1977). Informant accuracy in social network data II. Human Communication Research, 4 (1 3 18.
11. Bernard, H. R., Killworth, P., Kronenfeld, D. Sailer, L. (1984). The problem of informant accuracy: The validity of retrospective data. Annual Review of Anthropology, 13, 495 517.
12. Bernard, H. R., Killworth, P. Sailer, L. (1979). Informant accuracy in social networks IV: A comparison of clique-level structure in behavioral and cognitive network data. Social Networks, 2, 191 218.
13. Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society, Series B, 36 (2 192 236.
14. Bollobás, B. (1998). Modern Graph Theory. New York : Springer.
15. Bonacich, P. (1972). Factoring and weighting approaches to clique identification. Journal of Mathematical Sociology, 2, 113 120.
16. Borgatti, S. P. (2007). NetDraw: Network visualization software. Software package. Harvard : Analytic Technologies.
17. Borgatti, S. P., Carley, K. Krackhardt, D. (2006). Robustness of centrality measures under conditions of imperfect data. Social Networks, 28, 124 136.
18. Borgatti, S. P. Everett, M. G. (1999). Models of core/periphery structures. Social Networks, 21, 375 395.
19. Borgatti, S. P., Everett, M. G. Freeman, L. C. (1999). UCINET 5.0, Version 1.00. Natick, NJ : Analytic Technologies.
20. Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of Mathematical Sociology, 25 (2 163 177.
21. Brandes, U. Erlebach, T., eds. (2005). Network Analysis: Methodological Foundations. Berlin : Springer-Verlag.
22. Brass, D. J. (1984). Being in the right place: A structural analysis of individual influence in an organization. Administrative Science Quarterly, 29, 519 529.
23. Brewer, D. (2000). Forgetting in the recall-based elicitation of personal networks. Social Networks, 22, 29 43.
24. Brown, L. D. (1986). Fundamentals of Statistical Exponential Families, with Applications in Statistical Decision Theory. Hayward, CA : Institute of Mathematical Statistics.
25. Burt, R. S. (1992). Structural Holes: The Social Structure of Competition. Cambridge, MA : Harvard University Press.
26. Butts, C. T. (1998). A Bayesian model of panic in belief. Computational and Mathematical Organization Theory, 4 (4 373 404.
27. Butts, C. T. (2000). The sna package for the R statistical computing system. Software library. Pittsburgh, PA. Available from http://erzuli.ss.uci. edu/R.stuff.
28. Butts, C. T. (2003). Network inference, error, and informant (in)accuracy: A Bayesian approach. Social Networks, 25 (2 103 140.
29. Butts, C. T. (2006a). Cycle census statistics for exponential random graph models (IMBS Technical Report MBS 06-05). Irvine, CA : Institute for Mathematical Behavioral Sciences, University of California, Irvine.
30. Butts, C. T. (2006b). Exact bounds for degree centralization. Social Networks, 28 (4 283 296.
31. Butts, C. T. (2007). Permutation models for relational data. Sociological Methodology, 37, 257 281.
32. Butts, C. T. Carley, K. M. (2001). Multivariate methods for interstructural analysis. CASOS Working Paper. Carnegie Mellon University, Pittsburgh, PA : Center for the Computational Analysis of Social and Organization Systems.
33. Butts, C. T. Carley, K. M. (2005). Some simple algorithms for structural comparison. Computational and Mathematical Organization Theory, 11 (4 291 305.
34. Butts, C. T. Pixley, J. E. (2004). A structural approach to the representation of life history data. Journal of Mathematical Sociology, 28 (2 81 124.
35. Carley, K. M. (1991). A theory of group stability. American Sociological Review, 56 (3 331 354.
36. Carley, K. M. (1997). Extracting team mental models through textual analysis. Journal of Organizational Behavior, 18, 533 538.
37. Carrington, P. J., Scott, J. Wasserman, S., eds. (2005). Models and Methods in Social Network Analysis. Cambridge : Cambridge University Press.
38. Choudhury, T. Pentland, A. (2003). Sensing and modeling human networks using the sociometer. Proceedings of the 7th IEEE International Symposium on Wearable Computers, pp. 216 222. New York : White Plains.
39. Cliff, A. D. Ord, J. K. (1973). Spatial Autocorrelation. London : Pion.
40. Costenbader, E. Valente, T. W. (2003). The stability of centrality measures when networks are sampled. Social Networks, 25, 283 307.
41. Crouch, B., Wasserman, S. Trachtenburg, F. (1998). Markov chain Monte Carlo maximum likelihood estimation for p* social network models. Paper presented at the XVIII International Sunbelt Social Network Conference; April 1998, Sitges, Spain.
42. Davis, J. A. Leinhardt, S. (1972). The structure of positive interpersonal relations in small groups. In : J. Berger, ed. Sociological Theories in Progress, Vol. 2, pp. 218 251. Boston, MA : Houghton Mifflin.
43. Davis, J. A. Smith, T. W. (1988). General Social Survey, 1988. Chicago, IL : National Opinion Research Center.
44. Degenne, A. Forsé, M. (1999). Introducing Social Networks. London : Sage.
45. Doreian, P. (1989). Two regimes of network autocorrelation. In : M. Kochen, ed. The Small World, pp. 280 295. Norwood : Ablex.
46. Doreian, P. (1990). Network autocorrelation models: Problems and prospects. In : I. D. A. Griffith, ed. Spatial Statistics: Past, Present, and Future, pp. 369 389. Ann Arbor, MI : Institute of Mathematical Geography.
47. Doreian, P., Batagelj, V. Ferlioj, A. (2005). Generalized Blockmodeling. Cambridge : Cambridge University Press.
48. Ebel, H., Mielsch, L. I. Bornholdt, S. (2002). Scale-free topology of e-mail networks. Physical Review E, 66, 035103.
49. Erdös, P. Rényi, A. (1960). On the evolution of random graphs. Public Mathematical Institute of Hungary Academy of Sciences, 5, 17 61.
50. Everett, M. G. Borgatti, S. P. (2005). Ego-network betweenness. Social Networks, 27 (1 31 38.
51. Festinger, L., Schachter, S. Back, K. (1950). Social Pressures in Informal Groups. Stanford, CA : Stanford University Press.
52. Frank, O. (1978). Sampling and estimation in large social networks. Social Networks, 1 (1 91 101.
53. Frank, O. (2005). Network sampling and model fitting. In : P. J. Carrington, J. Scott S. Wasserman, eds. Models and Methods in Social Network Analysis, Chapter 3, pp. 31 56. Cambridge : Cambridge University Press.
54. Frank, O. Strauss, D. (1986). Markov graphs. Journal of the American Statistical Association, 81, 832 842.
55. Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1 (3 223 258.
56. Freeman, L. C. (2000). Visualizing social networks. Journal of Social Structure, 1 (1).
57. Freeman, L. C. (2004). The Development of Social Network Analysis: A Study in the Sociology of Science. Vancouver : Empirical Press.
58. Freeman, L. C., Fararo, T. J., Bloomberg, W. J. Sunshine, M. H. (1963). Locating leaders in local communities: A comparison of some alternative approaches. American Sociological Review, 28, 791 798.
59. Freidkin, N. (1998). A Structural Theory of Social Influence. Cambridge : Cambridge University Press.
60. Friedkin, N. Cook, K. S. (1991). Peer group influence. Sociological Methods and Research, 19, 122 143.
61. Friedkin, N. Johnsen, E. C. (1990). Social influence and opinions. Journal of Mathematical Sociology, 15, 193 206.
62. Fruchterman, T. Reingold, E. (1991). Graph drawing by force-directed placement. Software - Practice and Experience, 21 (11 1129 1164.
63. Goffman, E. (1961). Asylums: Essays on the Social Situation of Mental Patients and Other Inmates. New York : Doubleday.
64. Good, P. (2000). Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses. New York : Springer.
65. Goodman, L. A. (1961). Snowball sampling. Annals of Mathematical Statistics, 32, 148 170.
66. Gould, R. Fernandez, R. (1989). Structures of mediation: A formal approach to brokerage in transaction networks. Sociological Methodology, 19, 89 126.
67. Hage, P. Harary, F. (1995). Eccentricity and centrality in networks. Social Networks, 17, 57 63.
68. Handcock, M. S. (2003). Statistical models for social networks: Inference and degeneracy. In : R. Breiger, K. M. Carley P. Pattison, eds. Dynamic Social Network Modeling and Analysis, pp. 229 240. Washington, DC : National Academies Press.
69. Handcock, M. S., Raftery, A. E. Tantrum, J. M. (2007). Model based clustering for social networks. Journal of the Royal Statistical Society, Series A, 170 (2 301 354.
70. Heckathorn, D. D. (1997). Respondent-driven sampling: A new approach to the study of hidden populations. Social Problems, 44 (2 174 199.
71. Heckathorn, D. D. (2002). Respondent-driven sampling ii: Deriving valid population estimates from chain-referral samples of hidden populations. Social Problems, 49 (1 11 34.
72. Heider, F. (1946). Attitudes and cognitive organization. Journal of Psychology, 21, 107 112.
73. Hoff, P. D., Raftery, A. E. Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97 (460 1090 1098.
74. Holland, P. W. Leinhardt, S. (1970). A method for detecting structure in sociometric data. American Journal of Sociology, 70, 492 513.
75. Holland, P. W. Leinhardt, S. (1972). Some evidence on the transitivity of positive interpersonal sentiment. American Journal of Sociology, 72, 492 513.
76. Holland, P. W. Leinhardt, S. (1975). The statistical analysis of local structure in social networks. Sociological Methodology, 6, 1 45.
77. Holland, P. W. Leinhardt, S. (1981). An exponential family of probability distributions for directed graphs (with discussion). Journal of the American Statistical Association, 76 (373 33 50.
78. Hubert, L. J. (1987). Assignment Methods in Combinatorial Data Analysis. New York : Marcel Dekker.
79. Hummon, N. P. Fararo, T. J. (1995). Assessing hierarchy and balance in dynamic network models. Journal of Mathematical Sociology, 20, 145 159.
80. Kadushin, C. (1982). Social density and mental health. In : P. V. Marsden N. Lin, eds. Social Structure and Network Analysis, pp. 147 158. Newbury Park, CA : Sage.
81. Kamada, T. Kawai, S. (1989). An algorithm for drawing general undirected graphs. Information Processing Letters, 31 (1 7 15.
82. Katz, L. Powell, J. H. (1953). A proposed index of conformity of one sociometric measurement to another. Psychometrika, 18, 249 256.
83. Killworth, P. D. Bernard, H. R. (1976). Informant accuracy in social network data. Human Organization, 35 (8 269 286.
84. Killworth, P. D. Bernard, H. R. (1979). Informant accuracy in social network data III: A comparison of triadic structure in behavioral and cognitive data. Social Networks, 2, 10 46.
85. Klau, G. W. Weiskircher, R. (2005). Robustness and resilience. In : U. Brandes T. Erlebach, eds. Network Analysis: Methodological Foundations, pp. 417 437. Berlin : Springer-Verlag.
86. Klovdahl, A. S. (1989). Urban social networks: Some methodological problems and possibilities. In : M. Kochen, ed. The Small World, pp. 176 210. Norwood : Ablex.
87. Koehly, L. M. Pattison, P. (2005). Random graph models for social networks: Multiple relations or multiple raters. In : P. J. Carrington, J. Scott S. Wasserman, eds. Models and Methods in Social Network Analysis, pp. 162 191. Cambridge : Cambridge University Press.
88. Koenker, R. W. Bassett, G. W. (1978). Regression quantiles. Econometrica, 46, 33 50.
89. Krackhardt, D. (1987a). Cognitive social structures. Social Networks, 9 (2 109 134.
90. Krackhardt, D. (1987b). QAP partialling as a test of spuriousness. Social Networks, 9 (2 171 186.
91. Krackhardt, D. (1988). Predicting with networks: Nonparametric multiple regression analyses of dyadic data. Social Networks, 10, 359 382.
92. Krackhardt, D. (1994). Graph theoretical dimensions of informal organizations. In : K. M. Carley M. J. Prietula, eds. Computational Organizational Theory, pp. 88 111. Hillsdale, NJ : Lawrence Erlbaum Associates.
93. Krackhardt, D. (1997). Organizational viscosity and the diffusion of controversial innovations. Journal of Mathematical Sociology, 22 (2 177 199.
94. Krackhardt, D. Stern, R. N. (1988). Informal networks and organizational crises: An experimental simulation. Social Psychology Quarterly, 51, 123 140.
95. Latané, B. (1981). The psychology of social impact. American Psychologist, 36 (4 343 356.
96. Lazega, E. (2001). The Collegial Phenomenon: The Social Mechanisms of Cooperation Among Peers in a Corporate Law Partnership. Oxford : Oxford University Press.
97. Leenders, T. T. A. J. (2002). Modeling social influence through network autocorrelation: Constructing the weight matrix. Social Networks, 24 (1 21 47.
98. Lorrain, F. White, H. C. (1971). Structural equivalence of individuals in social networks. Journal of Mathematical Sociology, 1, 49 80.
99. McGrath, C., Blythe, J. Krackhardt, D. (1997). The effect of spatial arrangement on judgments and errors in interpreting graphs. Social Networks, 19 (3 223 242.
100. Mardia, K. V., Kent, J. T. Bibby, J. M. (1979). Multivariate Analysis. London : Academic Press.
101. Marsden, P. V. (1990). Network data and measurement. Annual Review of Sociology, 16, 435 463.
102. Marsden, P. V. (2005). Recent developments in network measurement. In : P. J. Carrington, J. Scott S. Wasserman, eds. Models and Methods in Social Network Analysis, pp. 8 30. Cambridge : Cambridge University Press.
103. Mayhew, B. H. (1984a). Baseline models of sociological phenomena. Journal of Mathematical Sociology, 9, 259 281.
104. Mayhew, B. H. (1984b). Chance and necessity in sociological theory. Journal of Mathematical Sociology, 9, 305 339.
105. Moody, J. White, D. R. (2003). Social cohesion and embeddedness. American Sociological Review, 68, 103 127.
106. Moreno, J. L. (1934). Who Shall Survive? Washington, DC : Nervous and Mental Disease Publishing Co.
107. Morris, M., ed. (2004). Network Epidemiology: A Handbook for Survey Design and Data Collection. Oxford : Oxford University Press.
108. Newcomb, T. (1953). An approach to the study of communicative acts. Psychological Review, 60, 393 404.
109. Newman, M. E. J. (2003). The structure and function of complex networks. SIAM Review, 45 (2 167 256.
110. Pattison, P. Robins, G. (2002). Neighborhood-based models for social networks. Sociological Methodology, 32, 301 337.
111. Pattison, P. Wasserman, S. (1999). Logit models and logistic regressions for social networks: II. multivariate relations. British Journal of Mathematical and Statistical Psychology, 52, 169 193.
112. Pattison, P., Wasserman, S., Robins, G. Kanfer, A. M. (2000). Statistical evaluation of algebraic constraints for social networks. Journal of Mathematical Psychology, 44, 536 568.
113. Pool, I. D. S. Kochen, M. (1979). Contacts and influence. Social Networks, 1 (1 5 51.
114. Rapoport, A. (1949a). Outline of a probabilistic approach to animal sociology I. Bulletin of Mathematical Biophysics, 11, 183 196.
115. Rapoport, A. (1949b). Outline of a probabilistic approach to animal sociology II. Bulletin of Mathematical Biophysics, 11, 273 281.
116. Rapoport, A. (1950). Outline of a probabilistic approach to animal sociology III. Bulletin of Mathematical Biophysics, 12, 7 17.
117. Richards, W. D. Seary, A. J. (2000). Eigen analysis of networks. Journal of Social Structure, 1 (1).
118. Robins, G. Pattison, P. (2005). Interdependencies and social processes: Dependence graphs and generalized dependence structures. In : P. J. Carrington, J. Scott S. Wasserman, eds. Models and Methods in Social Network Analysis, pp. 192 214. Cambridge : Cambridge University Press.
119. Robins, G., Pattison, P. Elliott, P. (2001). Network models for social influence processes. Psychometrika, 66, 161 190.
120. Robins, G., Pattison, P. Wasserman, S. (1999). Logit models and logistic regressions for social networks III. Valued relations. Psychometrika, 64, 371 394.
121. Robins, G., Pattison, P. Woolcock, J. (2005). Small and other worlds: Network structures from local processes. American Journal of Sociology, 110 (4 894 936.
122. Romney, A. K., Weller, S. C. Batchelder, W. H. (1986). Culture as consensus: A theory of culture and informant accuracy. American Anthropologist, 88 (2 313 338.
123. Scott, J. (1991). Social Network Analysis: A Handbook. London : Sage.
124. Seary, A. J. Richards, W. D. (2003). Spectral methods for analyzing and visualizing networks: An introduction. In : R. Breiger, K. M. Carley P. Pattison, eds. Dynamic Social Network Modeling and Analysis, pp. 209 228. Washington, DC : National Academies Press.
125. Shimbel, A. (1953). Structural parameters of communication networks. Bulletin of Mathematical Biophysics, 15, 501 507.
126. Snijders, T. A. B. (1981). The degree variance: An index of graph heterogeneity. Social Networks, 3 (3 163 223.
127. Snijders, T. A. B. (1991). Enumeration and simulation methods for 0-1 matrices with given marginals. Psychometrika, 56, 397 417.
128. Snijders, T. A. B. (1996). Stochastic actor-oriented models for network change. Journal of Mathematical Sociology, 23, 149 172.
129. Snijders, T. A. B. (2002). Markov Chain Monte Carlo estimation of exponential random graph models. Journal of Social Structure, 3 (2).
130. Strang, G. (1988). Linear Algebra and Its Applications, 3rd edn. Fort Worth, TX : Harcourt Brace Jovanovich.
131. Strauss, D. (1986). On a general class of models for interaction. SIAM Review, 28 (4 513 527.
132. Sussman, G. J. Wisdom, J. (2001). Structure and Interpretation of Classical Mechanics. Cambridge, MA : MIT Press.
133. Thompson, S. K. (1997). Adaptive sampling in behavioral surveys. In : L. Harrison A. Hughes, eds. The Validity of Self-Reported Drug Use: Improving the Accuracy of Survey Estimates, pp. 296 319. Rockville, MD : National Institute of Drug Abuse.
134. Thompson, S. K. Frank, O. (2000). Model-based estimation with link-tracing sampling designs. Survey Methodology, 26 (1 87 98.
135. Tierney, L. (1996). Introduction to general state-space Markov chain theory. In : W. Gilks, S. Richardson D. J. Spiegelhalter, eds. Markov Chain Monte Carlo in Practice, pp. 59 74. London : Chapman & Hall.
136. Tobin, J. (1958). Estimation of relationships for categorical and limited dependent variables. Econometrica, 26, 24 36.
137. Torgerson, W. S. (1952). Multidimensional scaling: I, theory and method. Psychometrika, 17, 401 419.
138. Travers, J. Milgram, S. (1969). An experimental study of the small world problem. Sociometry, 32, 425 443.
139. Tufte, E. R. (1983). The Visual Display of Quantitative Information. Cheshire, CT : Graphics Press.
140. Valente, T. W. (2005). Network models and methods for studying the diffusion of innovations. In : P. J. Carrington, J. Scott S. Wasserman, eds. Models and Methods in Social Network Analysis, pp. 98 116. Cambridge : Cambridge University Press.
141. Wasserman, S. (1987). Conformity of two sociomatrices. Psychometrika, 52, 3 18.
142. Wasserman, S. Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge : Cambridge University Press.
143. Wasserman, S. Pattison, P. (1996). Logit models and logistic regressions for social networks: I. An introduction to Markov graphs and p*. Psychometrika, 60, 401 426.
144. Wasserman, S. Robins, G. (2005). An introduction to random graphs, dependence graphs, and p*. In : P. J. Carrington, J. Scott S. Wasserman, eds. Models and Methods in Social Network Analysis, pp. 192 214. Cambridge : Cambridge University Press.
145. Watts, D. J. Strogatz, S. H. (1998). Collective dynamics of 'small-world' networks. Nature, 393, 440 442.
146. Wegner, D. M. (1995). A computer network model of human transactive memory. Social Cognition, 13 (3 313 339.
147. West, D. B. (1996). Introduction to Graph Theory. Upper Saddle River, NJ : Prentice Hall.
148. Willer, D., ed. (1999). Network Exchange Theory. Westport, CN : Praeger.
149. Wimmer, A. Min, B. (2006). From empire to nation-state: Explaining wars in the modern world, 1816-2001. American Sociological Review, 71 (6 867 897.
150. Yancey, W. L. (1971). Architecture, interaction, and social control: The case of a large-scale public housing project. Environment and Behavior, 3, 3 21.