Student Award (annual)
The Best Student Paper Award is given annually to a graduate student in recognition of research on social network analysis.
The award is for research by a graduate student and is administered by INSNA, the International Network for Social Network Analysis. For the 2012 competition, students should submit a paper (pdf file written in English) to the committee before 15 December 2011. The winner of the award will be announced at Sunbelt XXXII in 2012 and will give a formal presentation at Sunbelt XXXIII in Hamburg, Germany in 2013. To be eligible, the student must be the first or sole) author on the submitted paper at the time of submission. The paper may be published or unpublished and must have been completed within two years of the submission deadline.
A letter of support should be submitted as well. Papers are evaluated by a committee of at least three judges. Their decision is final and is based on the level of originality in the ideas and techniques, the possible applications and their treatment, and potential impact. The committee may arrive at the conclusion that none of the submitted papers merits the award.
The monetary prize for the best student paper is $2,500
Please send all application materials to INSNA President, Professor John Skvoretz, jskvoretz@usf.edu.
2011 Winner - Lea Ellwardt
"The Co-evolution of Gossip and Friendship at Work: Studying the Dynamics of Multiplex Social Networks" Lea EllwardtUniversity of Groningen/ICS
Christian Steglich
University of Groningen/ICS
Rafael Wittek
University of Groningen/ICS
This study investigates the co-evolution of friendship and gossip in organizations. Two contradicting theories are tested. Social capital theory predicts that friendship causes gossip between employees, defined as informal evaluative talking about absent colleagues. Evolutionary theory reverses this causality claiming that gossiping facilitates friendship. The data comprises of three observations of a complete organizational network, allowing longitudinal social network analyses and causal inferences. Gossip and friendship are modeled as both explanatory and outcome networks with Multiple SIENA. Results support evolutionary theory, as gossip increases friendship formation in dyads. However, high gossip activity decreases the number of friends in the group.
2010 Winner - Christoph Stadtfeld
"Analysing Event Stream Dynamics in Two Mode Networks"Christophe Stadtfeld
KIT Karlsruhe Institute of Technology
Andreas Geyer-Schulz
KIT Karlsruhe Institute of Technology
Exponential Random Graph Models (ERGMs) are widely used to describe structural network dynamics using panel data. In this paper the private communication behaviour within a question and answer community is analysed. Using this example, it is shown how event based extensions of the standard ERGM models can be applied on combined communication and affiliation networks. It is tested in how far communication patterns and affiliation patterns influence the choice of message receivers. To visualize change and evolution in event based networks, a sliding window approach is introduced and discussed.
2009 Winner - Krista J. Gile
"Respondent-Driven Sampling: An Assessment of Current Methodology"Krista J. Gile
University of Washington
Mark Handcock
University of Washington
Respondent-Driven Sampling (RDS) employs a variant of a link-tracing network sampling strategy to collect data from hard-to-reach populations. By tracing the links in the underlying social network, the process exploits the social structure to expand the sample and reduce its dependence on the initial (convenience) sample. Current estimation focuses on estimating population averages in the hard-to-reach population. These estimates are based on strong assumptions allowing the sample to be treated as a probability sample. In particular, we focus on three critical sensitivities of the estimators: to the without-replacement structure of sampling, to bias induced by the initial sample, and to uncontrollable features of respondent behavior. First, estimates are based on a with-replacement random walk model, while the actual sampling is without replacement. We illustrate that when over half of the target population is sampled this approximation can lead to substantial bias in the resulting estimators. Previous research on the properties of RDS estimators has not considered this assumption. Second, we address the reduction of bias induced by the convenience sampling of the initial sample. RDS relies on many sample waves to create a type of mixing in the sampling process, much like the mixing in a Markov chain. We illustrate that the number of sample waves typically used in RDS is likely insufficient for the type of nodal mixing required to obtain the reputed asymptotic unbiasedness of the estimators. In some cases, however, we find that despite this, the resulting estimators are approximately unbiased, although this is highly sensitive to the degree of clustering in the population and the number of waves in the sample. Finally, we highlight the dependence of the estimators on characteristics of respondent behavior outside the control of the researcher. In particular, we illustrate the bias induced in the estimator by preferential coupon-passing behavior by respondents. We highlight the need to expand data collection to learn more about how respondents behave in RDS studies. This paper sounds a cautionary note for the users of RDS. While current RDS methodology is powerful and clever, the good statistical properties claimed for the current estimates are shown to be heavily dependent on often unrealistic assumptions.
