LISTSERV mailing list manager LISTSERV 16.0

Help for SOCNET Archives


SOCNET Archives

SOCNET Archives


SOCNET@LISTS.UFL.EDU


View:

Message:

[

First

|

Previous

|

Next

|

Last

]

By Topic:

[

First

|

Previous

|

Next

|

Last

]

By Author:

[

First

|

Previous

|

Next

|

Last

]

Font:

Proportional Font

LISTSERV Archives

LISTSERV Archives

SOCNET Home

SOCNET Home

SOCNET  April 2006

SOCNET April 2006

Subject:

sna and abm list....

From:

Christina Prell <[log in to unmask]>

Reply-To:

Christina Prell <[log in to unmask]>

Date:

Fri, 28 Apr 2006 12:35:04 +0100

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (350 lines)

*****  To join INSNA, visit http://www.insna.org  *****

Hello all,

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
http://jasss.soc.surrey.ac.uk/JASSS.html

Rolfe, Meredith. n.d. Social Networks and Threshold Models of Collective 
Behavior <http://home.uchicago.edu/%7Emrrolfe/net_threshold.v2.pdf>
http://home.uchicago.edu/%7Emrrolfe/net_threshold.v2.pdf

Rolfe, Meredith. n.d. Interrogating the Usual Suspects: Education and 
Voter Turnout <http://home.uchicago.edu/%7Emrrolfe/usual_educ.pdf>
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 
http://www.casos.cs.cmu.edu/publications/papers.php
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 
M.P. Becker.
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, 
edited by
Kees van Montfort, Han Oud and Albert Satorra; Lawrence Erlbaum, 2006.
http://stat.gamma.rug.nl/snijders/chapter_coevol.pdf

Snijders, Tom A.B. and Van Duijn, Marijtje A.J. (1997). Simulation for 
statistical
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.
http://stat.gamma.rug.nl/snijders/SSP_total.pdf

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 
Organizations:
Computational Models of Institutions and Groups. Cambridge, MA: MIT

Carley, K. M., & Hill, V. (2001). “Structural Change and Learning Within 
Organizations.”
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: 
<http://www.xjtek.com/files/papers/diffusiondynamics2005.pdf>
Comparing Agent-Based and Differential Equation Models 
<http://www.xjtek.com/files/papers/diffusiondynamics2005.pdf> (PDF: 259Kb)
http://www.xjtek.com/files/papers/diffusiondynamics2005.pdf

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 
interpretation.
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 
findings
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 
fact
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 
the
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 
individuals
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 
expansiveness
bias may commonly distort findings concerning characteristics of individual
networks (e.g. size, range, density), and properties of whole networks 
(e.g.
inequality, transitivity, clustering, and blockmodels). We suggest
methodological improvements and urge further research. (C) 2002 
Published by
Elsevier Science B.V.

Galaskiewicz, J. and S. Wasserman (1993). "Social Network Analysis -
Concepts, Methodology, and Directions for the 1990s." Sociological 
Methods &
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 
five
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 
overview of
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 
costly
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 
for
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 
redesigned to
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 
personal
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 
any
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
26(4): 289-307.

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 
the
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 
may
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 
the
first-order zone are 1 by definition. The extent to which egocentric and
sociocentric versions of Freeman's betweenness centrality measure 
correspond
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 
research
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 
foundations.

Murty, S. A. (1999). "Setting the boundary of an interorganizational
network: An application." Journal of Social Service Research 24(3-4): 
67-82.

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 
been
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 
versus Cognition:
The Interaction of Agent Cognition and Organizational Design on 
Organizational Performance,
" Journal of Artificial Societies and Social Simulation, 1(3):1-19.30 
June 1998 at
<http://www.soc.surrey.ac.uk/JASSS/, 1: paper4.

_____________________________________________________________________
SOCNET is a service of INSNA, the professional association for social
network researchers (http://www.insna.org). To unsubscribe, send
an email message to [log in to unmask] containing the line
UNSUBSCRIBE SOCNET in the body of the message.

Top of Message | Previous Page | Permalink

Advanced Options


Options

Log In

Log In

Get Password

Get Password


Search Archives

Search Archives


Subscribe or Unsubscribe

Subscribe or Unsubscribe


Archives

September 2019
August 2019
July 2019
June 2019
May 2019
April 2019
March 2019
February 2019
January 2019
December 2018
November 2018
October 2018
September 2018
August 2018
July 2018
June 2018
May 2018
April 2018
March 2018
February 2018
January 2018
December 2017
November 2017
October 2017
September 2017
August 2017
July 2017
June 2017
May 2017
April 2017
March 2017
February 2017
January 2017
December 2016
November 2016
October 2016
September 2016
August 2016
July 2016
June 2016
May 2016
April 2016
March 2016
February 2016
January 2016
December 2015
November 2015
October 2015
September 2015
August 2015
July 2015
June 2015
May 2015
April 2015
March 2015
February 2015
January 2015
December 2014
November 2014
October 2014
September 2014
August 2014
July 2014
June 2014
May 2014
April 2014
March 2014
February 2014
January 2014
December 2013
November 2013
October 2013
September 2013
August 2013
July 2013
June 2013
May 2013
April 2013
March 2013
February 2013
January 2013
December 2012
November 2012
October 2012
September 2012
August 2012
July 2012
June 2012
May 2012
April 2012
March 2012
February 2012
January 2012
December 2011
November 2011
October 2011
September 2011
August 2011
July 2011
June 2011
May 2011
April 2011
March 2011
February 2011
January 2011
December 2010
November 2010
October 2010
September 2010
August 2010
July 2010
June 2010
May 2010
April 2010
March 2010
February 2010
January 2010
December 2009
November 2009
October 2009
September 2009
August 2009
July 2009
June 2009
May 2009
April 2009
March 2009
February 2009
January 2009
December 2008
November 2008
October 2008
September 2008
August 2008
July 2008, Week 62
July 2008
June 2008
May 2008
April 2008
March 2008
February 2008
January 2008
December 2007
November 2007
October 2007
September 2007
August 2007
July 2007
June 2007
May 2007
April 2007
March 2007
February 2007
January 2007
December 2006
November 2006
October 2006
September 2006
August 2006
July 2006
June 2006
May 2006
April 2006
March 2006
February 2006
January 2006
December 2005
November 2005
October 2005
September 2005
August 2005
July 2005
June 2005
May 2005
April 2005
March 2005
February 2005
January 2005
December 2004
November 2004
October 2004
September 2004
August 2004
July 2004
June 2004
May 2004
April 2004
March 2004
February 2004
January 2004
December 2003
November 2003
October 2003
September 2003
August 2003
July 2003
June 2003
May 2003
April 2003
March 2003
February 2003
January 2003
December 2002
November 2002
October 2002
September 2002
August 2002
July 2002
June 2002
May 2002
April 2002
March 2002
February 2002
January 2002
December 2001
November 2001
October 2001
September 2001
August 2001
July 2001
June 2001
May 2001

ATOM RSS1 RSS2



LISTS.UFL.EDU

CataList Email List Search Powered by the LISTSERV Email List Manager