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Dynamics of
Networks and Behavior
Satellite Symposium
May 10-11, 2004
connected to the
XXIV Sunbelt Conference
Portorož, Slovenia
May 12-16, 2004
Programme
Location:
Asteria Hall, Hotel Histrion, Portorož
Abstracts
(in order of surnames of first contributors)
Skye Bender-deMoll and Daniel McFarland,
School of Education, Stanford University.
Interaction, Time, and Motion: Animating Social Networks with SoNIA.
We consider some issues which arise with the aggregation of continuous-time
relational data ("streaming" interactions) to form network series. We demonstrate
SoNIA (Social Network Image Animator) as a tool for constructing animations of
dynamic networks (continuous or discrete), browsing attribute-rich network data, and
as a platform for testing and comparing layouts and techniques. We discuss file
formats for dynamic network data, strengths and weakness of existing layout
algorithms and suggestions for adapting them to sequential layout tasks.
Marc Boulay,
Center for Communication Programs,
Johns Hopkins Bloomberg School of Public Health.
Program-stimulated change in network composition and behavior.
This paper will examine the effects of a family planning communication program on
changes in social network composition and contraceptive use in Ghana. Numerous
studies have observed that health communication programs often stimulate
interpersonal communication about the program's messages. However, the effect of
this additional communication on the composition of a person's discussion netwo rk,
and consequently, the information and normative environment in which individuals'
decisions are made, remains unclear. Increased communication with existing
network partners or the homophilous selection of additional network partners may
simply reinforce existing normative pressures on behav ior. Alternatively, programs
may stimulate communication with a more diverse set of discussion par tners,
increasing individuals' access to information and embedding them in less restrictive
normative environments. These changes in network composition may moderate the
effect of the communication program on behavior change. To investigate the dynamic
relationships between a health communication program, network composition, and
behavior, data were collected from a census sample of members of 9 women's
groups in Ghana at two points in time (panel N=280). These data include measures
of exposure to a family planning communication program, contraceptive use status,
and the sociometric discussion patterns among the group members. The analyses
will explore the changes in discussion ne tworks over time, the effect of program
exposure on these changes, and the moderating effect of net work composition on
contraceptive adoption and sustained use.
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Vincent Buskens and Jeroen Weesie,
ICS / Department of Sociology, Utrecht University.
The emergence of social conventions.
In the literature on evolutionary game theory there are numerous explications of the
emergence of conventions unstructured populations (see Ellison 1993). Conventions
are studied in the context of coordination games in which the payoff dominant
equilibrium differs from the risk dominant equilibrium. Some recent work including
Berninghaus et al. (2002) and Buskens and Snijders (2004) addresses how social
structure affects this dynamic process. Here social networks take the form of
exogenously specified static dyadic relations shaping reference groups. In this new
paper we will extend the analysis in two ways. First, we will study in more details the
social conditions (e.g., in terms of network segmentation) under which a single or
multiple conventions emerge. Second, we will endogenize the network by making the
ties themselves object of choice.
Susan T. Ennett, Karl E. Bauman, Vangie A. Foshee, Andrea Hussong,
Rob DuRant, Patrick Curran, and Bob Faris,
Department of Health Behavior and Health Education,
University of North Carolina, Chapel Hill.
Social Network Characteristics and Drug Use:
Findings from a Longitudinal Study of Adolescent Health Risk
Behaviors.
Adolescents in North Carolina are being studied to enhance understanding of the
context of adolescent use of cigarettes, alcohol, inhalants, marijuana, and other
drugs. More than 5,000 students in grades 6-8 were first surveyed in Spring 2002
(Wave 1), and the cohort was surveyed again in Fall 2002 (Wave 2) and Spring 2003
(Wave 3). Selected Wave 1 social network indicators of popularity,
embeddedness/solidarity and substance use insulation are described and used as
predictors of subsequent drug use.
Eugene Johnsen,
University of California at Santa Barbara.
Social Influence Networks and Group Dynamics:
Attitude Change and Behavior.
In a social group the formation and change of personal attitudes on important issues
and the impact of these attitudes on behavior are of fundamental interest in the group
dynamics tradition of social psychology. Here we present a mathematical model
which describes and predicts the formation and change of personal attitudes in a
group on issues with which the group must deal.
The model is specified in terms of initial individual attitudes which are then subject to
interpersonal influences according to an influence process operating within the
network structure of the group. The initial attitudes and the dynamics of the process
are based on social psychological assumptions which are sufficient to specify the
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form of the model. The model permits a dynamically changing influence network and
allows both consensus and dissensus in the final individual attitudes on an issue,
which meets an objection about earlier forms of influence models which essentially
allowed only consensus to be formed. We discuss some implications of the model for
attitudes and behavior and bring the model to bear on some empirical data from a
study of small groups of sizes 2, 3 and 4.
Andrea Knecht and Chris Baerveldt,
ICS / Department of Sociology, Utrecht University.
Friendship and Delinquency of Adolescents.
A common finding of earlier studies on friendship and delinquent behavior of
adolescents is that delinquent behavior tends to be more similar among friends. How
can this be explained? In principle, two processes exists that lead to this result.
Either the adolescents have selected each other as friends because their
delinquency levels are similar, or friends adjust their behavior. Studies compare
those two processes are rare. We have been collecting complete longitudinal
network data (four waves) in 126 classes in Dutch secondary schools. For the
analysis of these data we use SIENA in order to compare the strength of the
selection and influence processes. We will present some preliminary results for a
subset of 20 networks.
Dan McFarland and Skye Bender-deMoll,
School of Education, Stanford University.
Classroom Structuration Tying Interaction Dynamics to Social Structure Stability.
A key aspect of schooling is the structuration process in classrooms. Classrooms are
social environments wherein actors coordinate interaction in a variety of ways over
the course of a single class period (e.g. from lecture to recitation to group work to
seat work). What social mechanisms lead classroom participants to adopt prescribed
forms of coordinated action, to routinize their interactions, and to adapt quickly to
new task demands? In short, what are the processes of structuration in classrooms
(i.e. process by which enacted practices become stable, structural forms) and how do
we study them directly? Direct empirical study of structuration is rather difficult and
especially if you agree with us that it concerns shifting interpersonal relations. In
order to research this process, we use a unique data set of over 700,000 streaming
interactions that span 600 class periods of 150 different classroom settings. We use
this data to create a variety of methods that enable us to get at the process of
change and routinization, and to predict whether certain causal levers are more
important than others. These methods are as follows: (1) a method for visualizing
network dynamics as movies (Bender-deMoll and McFarland 2003; Moody,
McFarland and Bender-deMoll 2003); (2) visual summaries and measurements of
network change (Butts and Carley 2003); and (3) multi-level growth curve models
used to identify mechanisms of change across many time points and observations
(Snijders and Bosker 1999). We believe these efforts have made situational
dynamics more interpretable and easier to analyze for a wide array of work settings
well beyond classroom settings.
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Andrej Mrvar and Vladimir Batagelj,
University of Ljubljana.
Visualization of Temporal Networks.
A graph G = (V, L) consists of a set of vertices (V) and a set of (directed or
undirected) lines L. The graph is called a network if some additional properties of
vertices/lines are defined (gender, capacity of link,...).
A temporal network is obtained if time T is attached to the ordinary network. T is a set
of linearly ordered time points t. Examples of temporal networks:
 network of friendship in the class in school over several years;
 changes in signed graphs over time (Sampson monastery data, Newcomb
fraternity);
 network of phone calls inside selected set of phone numbers (used by the
police in the investigation of organized crime);
 citation networks from a selected scientific area;
 network of transitions of a ball in a football game;
 changes in HIV networks;
 relations among actors in different episodes of movies;
 births, marriages and deaths in genealogies...
In a temporal network vertices and lines are not necessarily always present (active).
We will present two approaches of describing temporal networks which are
implemented in the program Pajek: functional description and description by time
events. In the functional description for each vertex and line the time points when
they are active are listed. The second possibility is description of development of
network in time by the sequence of time events, like add vertex, add line, show/hide
vertex/line, delete vertex/line change properties of vertices/lines . We will also
present some approaches to visualisation of such networks implemented in Pajek.
Marko Pahor,
University of Ljubljana.
Effects of corporate networks on company's performance.
Companies don't act independently from one another, but instead they form ties with
other companies. They don't form these ties randomly - they are carefully chosen in
order to maximize company's social capital. This social capital as a productive
means has the possibility of increasing company's performance.
Social capital is produced in networks in two ways - as network closure and
as network possibilities. Although the first one is usually associated with dense
networks, which would normally preclude the existence of brokering possibilities (or
structural holes), they can coexist in a network that has the characteristics of a "small
world" network. In a small world network, locally dense cluster in a generally sparse
network exist.
How the social capital will be produced for a company depends on the
characteristics of company's egonetwork, which is in turn dependent on the general
strategy of the company. For a company that chooses cost-leadership strategy, a
mechanistic egonetwork is more appropriate, and the social capital will be produced
in the form of network closure. On the other hand, companies that choose
differentiation as their generic strategy, will find advantage in maintaining an organic
network and will gain social capital from brokering possibilities.
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The theory will be modeled in the framework for the joint evolution of
networks and behavior. Company's properties, that affect and also reveal the chosen
strategy, also affect the selection of ties to other companies, giving the company
more network closure or more brokering possibilities. Network configuration in turn
affects company's performance, measured by the added value of the company. The
model will be tested on data for Slovenian joint-stock companies in the period 19982002.
Michael Pearson,
Centre for Mathematics and Statistics, Napier University, Edinburgh.
Dynamic Effects in Drifting Smoke Rings.
Social network analysis is applied to three time points of a longitudinal study, which
examines how risk-taking (represented by smoking and cannabis use) in
adolescence is associated with social position within peer group structures. One
hundred and fifty two students in the second year of secondary education in one
Scottish school named up to six best friends, allowing for the categorization of each
adolescent as a group member, a group peripheral or a relative isolate. Building on
previous work, it is shown that transitions from non risk-taking to risk-taking behavior
occur predominantly at peer group, rather than peripheral or isolate membership
level. The transitions of pupils from time point one through to time point three are
modeled as a Markov process, based on the assumption that the social position and
risk-taking behavior (or transitional state) of a pupil at a certain time point depends
only on their state at the previous time point. The results show that the Markov
process is not stationary. The expected length of time spent by pupils in the various
transitional states is also modeled, and provides another (time) dimension to the
influence of peers on risk-taking behavior. We hypothesize that the influence
exercised by an individual in a social network context increases with the
cohesiveness of the individual's social network position and the length of time he or
she occupies that position.
Further work carried out on the same data set made use of the actor oriented
approach of Tom Snijders. This enables dynamic network and behavioural effects to
be modelled, so that questions of influence and selection can be more closely
scrutinised. The rate functions of the network and behavioural states are also
investigated.
Arno Riedl and Aljaz Ule,
Tinbergen Institute and University of Amsterdam, CREED,
Faculty of Economics and Econometrics.
Cooperation, Exclusion, and Social Structure in Network Experiments.
This study examines the evolution of social networks within groups of six subjects
playing a prisoner's dilemma game. In each round subjects cannot discriminate in
their action choice but can exclude others from their social environment. Four
treatments are considered, varying the cost of exclusion and the information flow
through the network. Observed cooperation levels strongly depend on the treatment
conditions but are always significantly higher, reaching 93 percent, compared with
the control treatment with predetermined network structure. In addition, the evolution
of social structure is analyzed in parallel to the dynamics of action choices. It appears
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that when cooperators stay connected in cliques separated from defectors,
cooperation remains high until the last rounds.
Garry Robins and Michael Johnston,
Department of Psychology, University of Melbourne.
Joint social selection and social influence models for social networks:
The interplay of ties and attributes.
In social influence models, where actors adopt attributes (e.g. attitudes, opinions,
disease) because of influence from their network partners, network ties can be
considered as fixed, with attributes as variable. In social selection processes, on the
other hand, because actors choose each other as social partners on the basis of
attributes, the attribute distribution is taken as fixed but network ties are considered
changeable. Social selection and social influence are not mutually exclusive and in
many situations will operate simultaneously, perhaps with one process more
pronounced than the other.
Within the exponential random graph (p*) framework, we develop a simple
version of joint selection/influence models, by assuming a single binary attribute
distributed across the nodes within a binary nondirected graph, with attribute values
and graph edges mutually contingent. In particular, we examine models without a
triangle parameter, so that clustering effects arise only from homophily. The final
model has five edge-based parameters, relating various combinations of attributes to
the presence or absence of edges.
We simulate this relatively simple model with various parameter values. We
investigate relevant graph distributions as if the graphs were without attributes on the
nodes, just as in actual network analysis we might examine a network with no
attribute measurements. If the real structure is emergent solely from the attributebased process of joint influence/selection, can we determine that it is different from
homogeneous non-attribute models, and in what way? Can we produce something
like a "real world" network, for instance, a "small world" graph with low density, high
clustering, and short average geodesics, but also with a long tail degree distribution?
If our simple model is sufficient to produce plausible networks, in what circumstances
do we need to model more complex structural effects such as clustering?
Michael Schweinberger,
ICS / Department of Sociology, University of Groningen.
Statistical Modeling of Network Dynamics Given Panel Data:
Goodness-of-fit.
A class of statistical models for longitudinal (complete) network data was proposed
by Snijders (2001). The network dynamics are modeled as continuous-time Markov
process. It is assumed that the continuous-time process is not observed, but
observations can be made at discrete time points. The method of moments--implemented with Markov chain Monte Carlo methods---is used to make statistical
inference about the latent continuous-time Markov process.
The present paper is concerned with the---up to date not addressed--question of goodness-of-fit---that is, whether a given model specification is supported
by the observations. It is assumed that a baseline model for network evolution is
specified based on substantive and statistical considerations. In the model space, its
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neighborhood (in some lose sense) is explored by testing the baseline model against
related (less stringent) models. A suitable test statistic is acquired by applying largesample results on regular estimation equations by Basawa (1985) to Neyman's
(1959) C(alpha) test statistic. Statistical goodness-of-fit tests are executed using
simulated as well as real-world data.
Tom Snijders,
ICS / Department of Sociology, University of Groningen.
Introduction to stochastic actor-oriented modeling of the simultaneous
dynamics of networks and behavior.
Individuals attune their behavior to the behavior of others to whom they are related;
and they relate to other individuals taking into account, inter alia, the behavior of
those individuals. This leads to intricate feedback processes between relations
between individuals, and their behavior. Examples are smoking initiation among
adolescents - and other substance use and abuse -, the formation of attitudes and
norms, the dynamics of fads, collaboration in organisations, etc. The dynamics of
social networks therefore is closely related to the dynamics of individual behavior,
attitudes, and performance. The empirical study of such processes, and trying to
disentangle the influence from relations on behavior and the influence from behavior
on relations, poses difficulties from the points of study design, theoretical modeling,
and statistical analysis.
Recently, statistical methods have been developed to analyze the dynamics
of social networks (Snijders, Sociological Methodology, 2001), based on stochastic
actor-oriented models. Such models can be extended to the situation where the
dynamics of the social network is coupled to the dynamics of the behavior of the
actors in the network, and where panel data on networks and behavior are available.
In this presentation, the principles of such models will be discussed.
Christian Steglich*, Tom Snijders* and Michael Pearson**,
* ICS / Department of Sociology, University of Groningen,
** Centre for Mathematics and Statistics, Napier University, Edinburgh.
The Statistical Analysis of the Dynamics of Networks and Behaviour:
An Application of SIENA to the Co-evolution of Adolescent Smoking and
Friendship Networks.
We propose a family of stochastic models for analysing the co-evolution of social
networks in a given group and the group members’ individual properties (behaviour
and attitudes). By fitting our models to complete network data (including individual
characteristics) that are measured in a panel design, we arrive at parameter
estimates that can be used for drawing inferences about the evolution processes.
The proposed method extends Snijders’ methodology for the analysis of ‘pure’
network dynamics (Snijders 2001, 1996) by a component that allows to include the
co-evolution of behaviour over time. It is implemented in an updated version of the
SIENA software.
In principle, the proposed method enables researchers to empirically separate
selection mechanisms from influence mechanisms, a question that is crucial for a
proper understanding of a variety of network autocorrelation phenomena. We
illustrate the method by applying it to an exemplary data set, a three-wave panel of
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friendship networks and smoking behaviour measured in 1995-97 at a school in
Scotland. The analysis will focus on the assessment and separation of selection and
influence mechanisms with respect to smoking behaviour.
Chunke Su*, Douglas Steinley**, Katherine J. Klein***, and
Noshir Contractor*,
* Department of Speech Communication,
** Department Psychology, University of Illinois at Urbana-Champaign;
*** Department of Psychology, University of Maryland.
Co-evolution of Team Members’ Attachment to the Team and Team
Interpersonal Networks.
This paper seeks to investigate the co-evolution of individual attributes and networks
of interpersonal relationships. Specifically, we focus on examining the mutual
influences between team members’ feelings of attachment to the team and their
friendship, advice-seeking and adversarial networks. We posit that changes in
individuals’ feelings of attachment to the team may cause changes in their friendship,
advice-seeking and adversarial relationships. On the other hand, we speculate that
the dynamics in team members’ friendship, advice-seeking and adversarial networks
may cause changes in their feelings of attachment to the team. In addition, we
attempt to explore the dynamic relationship between the similarity or difference in
individual attributes and team members’ interpersonal networks. In other words, this
paper explores whether and how similarity in individuals’ feelings of attachment to
their team is related to team friendship, advice-seeking and adversarial networks
over time. Data was collected from three teams (10 individuals each) at three points
in time over a period of ten months. An actor-oriented dynamic statistical network
model is employed to test the hypothesized relationship between the dynamics of
individual attributes and interpersonal networks.
Hein de Vries and Juliette Rahman,
Department of Health Promotion and Health Education, University of
Maastricht.
Processes of Friend Influence and Friend Selection in the Development
of Adolescent Smoking Behavior.
The social influence paradigm assumes that the influence of friends play an
important role in the development of smoking behavior in adolescents. However,
similarities in smoking behavior can also be the result of interpersonal selection
processes. Adolescents tend to acquire new friends with similar smoking behavior.
The current paper focused upon techniques to uncover influence and selection
processes among Dutch adolescents. Longitudinal data on social networks were
collected in schools who participated in the European Smoking prevention
Framework Approach (ESFA). Differences between adolescents with stable and
unstable friendships will be discussed.
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Schedule of presentations
Monday May 10th
9:00 Coffee
9:10 Snijders
9:50 Pearson
10:30 Steglich, Snijders &
Pearson
11:10 Coffee Break (10 min)
11:20 Bauman & Faris
12:00 de Vries & Rahman
12:40 Lunch (1½ h)
14:10 Buskens & Weesie
14:50 Riedl & Ule
15:30 Coffee Break (10 min)
15:40 Pahor
16:20 Schweinberger
Tuesday May 11th
9:00 McFarland & Bender-deMoll
9:40 Bender-deMoll & McFarland
10:20 Coffee Break (10 min)
10:30 Knecht & Baerveldt
11:10 Boulay
11:50 Coffee break (10 min)
12:00 Mrvar & Batagelj
12:40 Su, Steinley, Klein &
Contractor
13:20 Lunch (1½ h)
14:50 Robins & Johnston
15:30 Johnsen
16:10 End
17:00 End
The symposium is associated to the research programme "Dynamics of Networks
and Behavior", a collaboration of researchers from the Universities of Groningen,
Utrecht and Maastricht. We gratefully acknowledge funding by the Netherlands
Organization for Scientific Research (NWO) under grant 401-01-550.
Organizers:
Tom Snijders ([email protected])
Christian Steglich ([email protected])
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