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Below is the list of references sent to me from various people on
SOCNET. Thank you to those who sent me this information.
I have not organized the references alphabetically, as some references
tend to have a bit of text or description attached, and as suggested
readings sometimes coincided with particular themes. So I have cut and
pasted the suggested readings based on the order in which they were
received through e-mail.
Best wishes, Christina
SNA AND ABM REFERENCES AS RECEIVED FROM SOCNET LIST
If it is ABM on static network, you could find many references in JASSS
Rolfe, Meredith. n.d. Social Networks and Threshold Models of Collective
Rolfe, Meredith. n.d. Interrogating the Usual Suspects: Education and
Voter Turnout <http://home.uchicago.edu/%7Emrrolfe/usual_educ.pdf>
The CASOS lab at CMU has been incorporating the two methodologies for
quite some time.
More papers can be found at
In particular Dr. Carley's papers will be relevant
- A Theory of Group Stability
- Dynamic Network Analysis
- Group Stability: A Socio-Cognitive Approach
- On the Evolution of Social and Organizational Networks
- Structural change and Learning Within Organizations
Snijders, T.A.B., Stochastic actor-oriented dynamic network analysis.
Journal of Mathematical Sociology, 21 (1996), 149-172.
Snijders, Tom A.B., The statistical evaluation of social network dynamics.
Pp. 361-395 in Sociological Methodology - 2001, edited by M.E. Sobel and
Boston and London: Basil Blackwell.
Snijders, Tom A.B (2003). Accounting for Degree Distributions in
Empirical Analysis of Network Dynamics.
Pp. 146-161 in: R. Breiger, K. Carley, and P. Pattison (eds.), Dynamic
Social Network Modeling and Analysis:
Workshop Summary and Papers.
National Research Council of the National Academies. Washington, DC: The
National Academies Press.
Snijders, Tom A.B. (2005). Models for Longitudinal Network Data.
Chapter 11 in P. Carrington, J. Scott, & S. Wasserman (Eds.), Models and
methods in social network analysis.
New York: Cambridge University Press.
Snijders, Tom A.B., Steglich, Christian E.G., and Schweinberger, Michael,
Modeling the co-evolution of networks and behavior.
To appear in Longitudinal models in the behavioral and related sciences,
Kees van Montfort, Han Oud and Albert Satorra; Lawrence Erlbaum, 2006.
Snijders, Tom A.B. and Van Duijn, Marijtje A.J. (1997). Simulation for
inference in dynamic network models.
In: Conte, R., Hegselmann, R. Terna, P. (eds.), Simulating social
phenomena , 493-512. Berlin: Springer.
Steglich, C.E.G., Snijders, T.A.B. and Pearson, M. (2004). Dynamic
Networks and Behavior:
Separating Selection from Influence.
Submitted for publication.
van de Bunt, G.G., Van Duijn, M.A.J., and Snijders, T.A.B., Friendship
networks through time:
An actor-oriented statistical network model.
Computational and Mathematical Organization Theory, 5 (1999), 167-192.
Prietula, M. J., Carley, K. M. & Gasser, L. (eds.) (1998). Simulating
Computational Models of Institutions and Groups. Cambridge, MA: MIT
Carley, K. M., & Hill, V. (2001). “Structural Change and Learning Within
In A. Lomi & E. R. Larsen (eds.), Dynamics of Organizational Societies:
Computational Modeling and
Organization Theories, Cambridge, MA: AAAI/MIT Press, 63–92.
Hazhir Rahmandad and John Sterman
Heterogeneity and Network Structure in the Dynamics of Diffusion:
Comparing Agent-Based and Differential Equation Models
<http://www.xjtek.com/files/papers/diffusiondynamics2005.pdf> (PDF: 259Kb)
THIS SECTION IS AN ANNOTATED BIBLIOGRAPHY PROVIDED BY CHRIS WEARE
Anderson, B. S., C. Butts, et al. (1999). "The interaction of size and
density with graph-level indices." Social Networks 21(3): 239-267.
The size and density of graphs interact powerfully and subtly with
other graph-level indices (GLIs), thereby complicating their
Here we examine these interactions by plotting changes in the distributions
of several popular graph measures across graphs of varying sizes and
densities. We provide a generalized framework for hypothesis testing as a
means of controlling for size and density effects, and apply this method to
several well-known sets of social network data; implications of our
for methodology and substantive theory are discussed. (C) 1999 Elsevier
Science B.V. All rights reserved.
Brewer, D. D. (2000). "Forgetting in the recall-based elecitation of
personal and social networks." Social Networks 22: 29-43.
Brewer, D. D. and C. M. Webster (1999). "Forgetting of friends and its
effects on measuring friendship networks." Social Networks 21: 361-373.
Butts, C. T. (2003). "Network inference, error, and informant (in)accuracy:
a Bayesian approach." Social Networks 25(2): 103-140.
Much, if not most, social network data is derived from informant
reports; past research, however, has indicated that such reports are in
highly inaccurate representations of social interaction. In this paper, a
family of hierarchical Bayesian models is developed which allows for the
simultaneous inference of informant accuracy and social structure in the
presence of measurement error and missing data. Posterior simulation for
these models using Markov Chain Monte Carlo methods is outlined. Robustness
of the models to structurally correlated error rates, implications of the
Bayesian modeling framework for improved data collection strategies, and
validity of the criterion graph are also discussed. (C) 2003 Elsevier
Science B.V. All rights reserved.
Campbell, K. E. and B. A. Lee (1991). "Name Generators in Surveys of
Personal Networks." Social Networks 13(3): 203-221.
To investigate the consequences of name generators for network data,
we compare characteristics of egocentric networks from Wellman's East York
survey, Fischer's Northern California Communities Study, the General Social
Survey, and our study of networks in 81 Nashville, Tennessee neighborhoods.
Network size, age and education heterogeneity, and average tie
characteristics were most strongly affected by the name generator used.
Network composition, and racial and sexual heterogeneity, were more
invariant across different kinds of name generators.
Doreian, P. and K. L. Woodard (1992). "Fixed List Versus Snowball Selection
of Social Networks." Social Science Research 21(2): 216-233.
Erickson, B. (1978). Some Problems of inference from chain data.
Sociological Methodology. K. F. Schuessler. San Francisco, Jossey-Bass.
Feld, S. L. and W. C. Carter (2002). "Detecting measurement bias in
respondent reports of personal networks." Social Networks 24(4): 365-383.
Inaccuracy of sociometric reports poses a serious challenge to
social network analysis. Nevertheless, researchers continue to draw
potentially misleading conclusions from flawed data. We consider two
particular types of systematic error in measurement of network size:
individuals over/underreporting others (expansiveness bias), and
being over/underreported by others (attractiveness bias). We examine
evidence of individual variation in these biases in one apparently typical
sociometric dataset. We specifically suggest that variation in
bias may commonly distort findings concerning characteristics of individual
networks (e.g. size, range, density), and properties of whole networks
inequality, transitivity, clustering, and blockmodels). We suggest
methodological improvements and urge further research. (C) 2002
Elsevier Science B.V.
Galaskiewicz, J. and S. Wasserman (1993). "Social Network Analysis -
Concepts, Methodology, and Directions for the 1990s." Sociological
Research 22(1): 3-22.
Network analysis has been used extensively in sociology over the
last twenty years. This special issue of Sociological Methods & Research
reviews the substantive contributions that network analysis has made to
areas: political sociology, interorganizational relations, social support,
social influence, and epidemiology. To introduce the novice to current
developments in the field, this introductory article presents an
the key concepts and methods which are popular among sociologists and which
have been used to advance knowledge in these substantive areas. Remaining
articles are also discussed briefly, with speculations offered on some of
the more promising avenues of inquiry recently under exploration.
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.
Researchers studying hidden populations-including injection drug
users, men who have sex with men, and the homeless-find that standard
probability sampling methods are either inapplicable or prohibitively
because their subjects lack a sampling frame, have privacy concerns, and
constitute a small part of the general population. Therefore, researchers
generally employ non-probability methods, including location sampling
methods such as targeted sampling, and chain-referral methods such as
snowball and respondent-driven sampling. Though nonprobability methods
succeed in accessing the hidden populations, they have been insufficient
statistical inference. This paper extends the respondent-driven sampling
method to show that when biases associated with chain-referral methods are
analyzed in sufficient detail, a statistical theory of the sampling process
can be constructed, based on which the sampling process can be
permit the derivation of indicators that are not biased and have known
levels of precision. The results are based on a study of 190 injection drug
users in a small Connecticut city.
Kogovsek, T. and A. Ferligoj (2004). "The quality of measurement of
support subnetworks." Quality & Quantity 38(5): 517-532.
Data about personal networks and their characteristics are
increasingly used in social science research, especially in research about
the quality of life, social support and similar topics (Fischer, 1982;
Marsden, 1987; van der Poel, 1993b). Since all data about a person's social
network are usually obtained from the respondent himself, the quality of
such measurements is a very important issue. Among other factors, the type
of social support can affect the quality of social network measurement
(Ferligoj and Hlebec, 1998, 1999). Differences in the stability of
measurement between the core and extended personal network have also been
found (Marsden, 1990; Morgan et al., 1997). The closer and the more
important an alter is, the more likely it is that (s)he will be named in
measurement (Hoffmeyer-Zlotnik, 1990; Van Groenou et al., 1990; Morgan et
al., 1997). In this paper the results of a recent study on the quality of
measurement of tie characteristics in different personal subnetworks are
presented. The Multitrait-multimethod (MTMM) approach was used for
estimating reliability and validity. A meta analysis of reliability and
validity estimates was done by hierarchical clustering. The data were
collected in the year 2000 by computer assisted face-to-face and telephone
interviews from a random sample of 1033 residents of Ljubljana.
Marin, A. (2004). "Are respondents more likely to list alters with certain
characteristics? Implications for name generator data." Social Networks
Analyses of egocentric networks make the implicit assumption that
the list of alters elicited by name generators is a complete list or
representative sample of relevant alters. Based on the literature on free
recall tasks and the organization of people in memory, I hypothesize that
respondents presented with a name generator are more likely to name alters
with whom they share stronger ties, alters who are more connected within
network, and alters with whom they interact in more settings. I conduct a
survey that presents respondents with the GSS name generator and then
prompts them to remember other relevant alters whom they have not yet
listed. By comparing the alters elicited before and after prompts I find
support for the first two hypotheses. I then go on to compare network-level
measures calculated with the alters elicited by the name generator to the
same measures calculated with data from all alters. These measures are not
well correlated. Furthermore, the degree of underestimation of network size
is related to the networks' mean closeness, density, and mean duration of
relationships. Higher values on these variables result in more accurate
estimation of network size. This suggests that measures of egocentric
network properties based on data collected using a single name generator
have high levels of measurement error, possibly resulting in misestimation
of how these network properties relate to other variables. (C) 2004
Published by Elsevier B.V.
Marsden, P. V. (1990). "Network Data and Measurement." Annual Review of
Sociology 16: 435-463.
Marsden, P. V. (2002). "Egocentric and sociocentric measures of network
centrality." Social Networks 24(4): 407-422.
Egocentric centrality measures (for data on anode's first-order
zone) parallel to Freeman's [Social Networks 1 (1979) 215] centrality
measures for complete (sociocentric) network data are considered.
Degree-based centrality is in principle identical for egocentric and
sociocentric network data. A closeness measure is uninformative for
egocentric data, since all geodesic distances from ego to other nodes in
first-order zone are 1 by definition. The extent to which egocentric and
sociocentric versions of Freeman's betweenness centrality measure
is explored empirically. Across seventeen diverse networks, that
correspondence is found to be relatively close-though variations in
egocentric network composition do lead to some notable differences in
egocentric and sociocentric betweennness. The findings suggest that
design has a relatively modest impact on assessing the relative betweenness
of nodes, and that a betweenness measure based on egocentric network data
could be a reliable substitute for Freeman's betweenness measure when it is
not practical to collect complete network data. However, differences in the
research methods used in sociocentric and egocentric studies could lead to
additional differences in the respective betweenness centrality measures.
(C) 2002 Elsevier Science B.V. All rights reserved.
Milardo, R. M. (1992). "Comparative Methods for Delineating Social
Networks." Journal of Social and Personal Relationships 9(3): 447-461.
By centering on the assumption that clear conceptualization precedes
appropriate measurement, four methods for defining and enumerating personal
networks are detailed. Global networks are defined in terms of the domain
from which all other personal networks are derived. The three additional
types, including significant other, exchange and interactive networks, are
conceptually unique and largely non-overlapping in their memberships. The
network types reviewed here do not exhaust all of the methods available for
sampling personal networks, but they do represent methods with favorable
psychometric properties and, most importantly, clear conceptual
Murty, S. A. (1999). "Setting the boundary of an interorganizational
network: An application." Journal of Social Service Research 24(3-4):
A method for setting the boundary of an interorganizational network
is described. This method is then applied to an interorganizational network
for disaster services. The results show that the method was successful at
identifying a larger and more varied network membership than would have
identified using other methods. Further research should apply the method to
various types of service networks in various settings.
Rothenberg, R. B. (1995). "Commentary: Sampling in Social Networks."
CONNECTIONS 18(1): 104-10.
Kathleen M. Carley, Michael J. Prietula and Zhiang Lin, 1998, "Design
The Interaction of Agent Cognition and Organizational Design on
" Journal of Artificial Societies and Social Simulation, 1(3):1-19.30
June 1998 at
<http://www.soc.surrey.ac.uk/JASSS/, 1: paper4.
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