Social Networks 32 (2010) 1–3
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Social Networks
journal homepage: www.elsevier.com/locate/socnet
Editorial
Introduction to the special issue on network dynamics
This journal issue contains the first of two connected special issues on Dynamics of Social Networks. This second special
issue will appear later this year. For a rather long time, attention
to dynamic aspects in Social Network Analysis took the form of
descriptive studies. However, over the last fifteen years modelbased approaches to studying network change have been flowering.
Landmarks were three special issues on Network Evolution of
the Journal of Mathematical Sociology, edited by Frans Stokman
and Patrick Doreian, in 1996 (with a book version: Doreian and
Stokman, 1997), 2001, and 2003. These three special issues demonstrated how formal and statistical modeling and empirical analysis
were coming together. The 2001 and 2003 special issues were
focused on joining of theoretical developments with the analysis of
empirical data using advanced modeling. This special issue presents
a continuation of jointly using theories and modeling to understand
social network phenomena.
In the first paper, Corten and Buskens (2010) adopt a gametheoretical approach to study how, in a well defined context,
behavioral choices as well as network ties can be changed by
actors. Their study unites various elements: evolution of conventions, network dynamics, game theory, computer simulations, and
laboratory experiments. This combination of approaches is relatively novel and very promising. In their tests of micro-level and
macro-level hypotheses, one of the findings is that there are clear
deviations from myopic best-response behavior, an often-used
heuristic in dynamic game theory. The authors interpret these deviations as signs of anticipatory behavior and as memory effects. A
major result of their paper is the demonstration of how human
actors, linked in an experimental setting for self-formed networks,
can coordinate their actions to produce efficient outcomes and that
this coordination was promoted by these deviations.
Next, Roth and Cointet (2010) examine the evolution of sociocognitive systems by considering the entwined dynamics of the
social network of citations and the semantic network of use of
concepts. In two contrasting cases, they study the network of
researchers of the Zebrafish and the network of bloggers in an early
phase of the 2008 US presidential election. This joint attention to
citations and semantics permits the study of the coevolution of a
one-mode and a two-mode network in an innovative and interesting fashion. For example, they find that the preference to form
links to high-degree nodes (cf. de Solla Price, 1976) holds in both
the citation and semantic networks. This effect is stronger for the
scientists than for the bloggers. Roth and Cointet discuss whether
this preference is to be interpreted as differential attractivity – one
could, perhaps, say status – or as differential activity, and suggest
that the latter is a better interpretation. Further they obtain clear
0378-8733/$ – see front matter © 2009 Published by Elsevier B.V.
doi:10.1016/j.socnet.2009.12.002
evidence for transitive processes as well as for semantic homophily.
Due (presumedly) to the difficulty of automatic extraction of opinions from text, they do not use information concerning allegiance
with, and opposition to, parties or candidates, which could be of
particular relevance for studing blog networks. Doing this will be a
major advance, and we hope this will be possible in the near future.
Conti and Doreian (2010) present a case study of network
dynamics in a ‘near-experimental’ pair of arrangements in a police
academy, set against the background of race relations, and the
perceived importance of this topic by police academy officials.
Their approach is hypothesis-driven, and they test hypotheses
about effects of squad memberships and seating arrangements on
social knowledge and friendship between recruits. They use a combination of ethnographic and quantitative techniques, including
blockmodeling, significance testing based on network-based bootstrapping and MRQAP permutation tests. The detailed knowledge
of the research site allowed the inclusion of rich information about
the infrastructure and the time heterogeneity of the social processes. Although this may strictly speaking limit generalizability,
as the paper itself mentions in the discussion section, nevertheless
the rich detail does tell a story about management of diversity in
teams which has a general relevance to teams in organizations.
The next five papers use actor-based models of network
dynamics, and of the co-evolution of networks and individual characteristics (‘behavior’), based on the principles laid out in Snijders
(2001) and Steglich et al. (in press). To set the stage, Snijders et
al. (2010) provide a tutorial introduction to these models. They
provide a way of testing hypotheses about network dynamics, representing several network evolution theories in an encompassing
model similar to what is done in other statistical models, such as
the linear regression model. This facilitates researchers including
‘control variables’, or multiple ‘independent variables’, in a single
model. At the same time, this modeling approach deals with the
dependencies that are implied by the fact that many different tie
variables are included in one given social network.
The four empirical papers using this method are ordered by the
age of the populations considered: they are set, respectively, in
the contexts of a pre-school, a high school, a Naval Academy, and
adult migration to a new country. Schaefer et al. (2010) attempt
to get close to the origin of networks by investigating networks of
preschool children, ranging in age from 3 to 5 years. How fundamental principles of network dynamics operate at these pristine
social and cognitive levels has been little studied previously. These
authors hypothesize a process called structural cascading, where
simpler structural processes precede more complicated ones. This
was, indeed, confirmed empirically: reciprocity, the simplest struc-
2
Editorial / Social Networks 32 (2010) 1–3
tural process, was important from the start and remained so with
little change; popularity (the Matthew effect, Merton, 1957), transitivity (Davis, 1970), and the tendency to forming complete triads
– all being more complicated structural processes, involving three
or more actors – all increased over time as the children grew older.
This set of results can be added to the result of Doreian et al. (1996)
who reported different (increasing) time scales for reciprocity,
transitivity and structural balance in a college residential setting.
Mercken et al. (2010) contribute a study about smoking initiation among adolescents. This can be regarded as a study into
network autocorrelation, i.e., the observation that friends tend to
be similar in smoking behavior. The fundamental theoretical and
methodological issue is whether the observed network autocorrelation is due to friends influencing each other or to adolescents
choosing friends who are similar to themselves (homophily, cf.
McPherson et al., 2001). Based on an extensive study of more than
1300 adolescents in Finland, they find strong support for both social
influence and for homophilous selection of friends. Intriguingly, the
latter was observed only for non-reciprocating choices. Social influence was not differentially strong when comparing reciprocal and
non-reciprocal friends. However, selection was found to contribute
more than influence to the observed network autocorrelation. This
study can be seen as a contribution to the strand in the literature
providing evidence for the insight that selection of friends is more
important than social influence for the similarity between friends
in smoking behavior (cf. Ennett and Bauman, 1994).
de Klepper et al. (2010), in the setting of a Naval Academy, study
the co-evolution of friendship and military discipline (compliance
with rules, and acceptance of authority), with a special focus on the
consequences of organizational constraints. For the co-evolution
of networks and changeable individual attributes, as studied also
by Mercken and co-authors, they propose an interesting general
hypothesis that visible individual attributes, such as overt behavior,
may be more likely to lead to selection, while non-visible individual attributes, such as attitudes and opinions, are more susceptible
to influence. In their hypotheses, they bring to bear three characteristics of the social setting of the Naval Academy: networks are
highly constrained; military discipline is one of the purposes of
the organizational setting; and discipline is a non-visible characteristic. These authors indeed find support for social influence with
respect to military discipline. Second, they do not find evidence for
homophilous friendship choice along this dimension. Finally, their
expectation that social influence is weighted by similarity in social
identity and by opportunities for peer control, was not empirically
corroborated.
In the last paper of this special issue, Lubbers et al. (2010) study
personal rather than entire networks. The personal networks were
among Argentinean immigrants in Spain. They used both qualitative and quantitative methods to examine the integration of the
immigrants in their host country in terms of both ego–alter and
alter–alter ties. On the one hand, this paper provides an interesting and innovative methodological illustration of the analysis of
change and persistence in personal network; on the other hand, it
is an insightful study of ways in which immigrants establish their
networks in a new country. The study underscores the importance
of employment for immigrants for creating ties in the new country.
Changes in ties were explained more strongly by tie characteristics
than by ego characteristics, thus confirming results obtained by
Wellman et al. (1997). An unexpected result was that, while ties in
denser networks on average were more stable, when controlling for
the strength and contact frequency of individual ties, ties in denser
networks were less stable. This contrast with Burt’s (2000) finding
that embeddedness in a dense network slows decay may be related
with the different populations (what holds for bankers does not
necessarily hold for immigrants). Another reason may be methodological because more adequate statistical methods were used by
Lubbers et al. They took into account the correlation between ties of
the same ego by conducting a multilevel analysis. As to design and
analysis, the paper calls for the development of further multilevel
methods to investigate the complex structure of the dynamics of
personal networks and the changing behavior and outcomes of the
individuals embedded in them.
We started this inroduction by noting the special issues of the
Journal of Mathematical Sociology on network evolution. In concluding, we return to the first of them to see if some of the considerations
that were important then still have a relevance today. Stokman
and Doreian (1997) outlined five principles for studies of network
evolution. For the papers in this special issue, it is clear that the
principle of simple models has been discarded in favor of various
kinds of computational models. However, the principle of models having sufficient empirical references is satisfied by all of the
papers presented in the current volume. For the other three principles the situation is less clear-cut. The principles of paying attention
to the goal structure of the actors, and to the information they are
assumed to use, receive some attention in the papers collected here.
Of course, further progress could be achieved by focusing more
on them but this may be difficult in naturally occurring settings.
The principle of actors acting, or optimizing, in parallel is followed
implicitly in the models used here – but the further possibility of
modeling the coordination between actors, e.g., in proposing and
accepting ties, was present in only the first paper of this special
issue.
Once, it was reasonable to note, and deplore, the essentially
static approach of social network analysis. The recent history of
the field shows that continued attention to network dynamics is
now a major feature of social network analysis. We hope, with network dynamics now being an integral part of mainstream social
network analysis, that future papers about dynamics and evolution of networks will be be published in regular issues rather than
being collected primarily in special issues devoted to these topics.
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Tom A.B. Snijders ∗
University of Oxford, UK
University of Groningen, The Netherlands
Patrick Doreian
University of Pittsburgh, United States
University of Ljubljana, Slovenia
∗ Corresponding
author.