SONIC lab is proud to welcome Julie Birkholz, who will present a talk on Monday, Dec 17, 2012 (10:30-11:45) in Frances Searle Building Room 1.483 on Northwestern’s Evanston Campus. All are welcome to attend.
About The Talk
Studies on social networks have proved that both structure and social attributes influence dynamics. Two streams of modeling exist to explain the dynamics of social networks: 1) models predicting links through network properties, and 2) models considering the effects of social attributes. In our current work we take an approach to work to overcome a number of computational limitations within these current models.We employ a mean-field model which allows for the construction of a population-specific model informed from empirical research for predicting links from both network and social properties in large social networks. The model is tested on a population of conference coauthorship behaviors of Dutch Computer Scientists, considering a number of parameters from available Web data. We prove that the mean-field model, using a data-aware approach, allows us to overcome computational burdens and thus scalability issues in modeling large social networks. A link to our current work can be found here – http://arxiv.org/abs/1209.6615.
About Julie Birkholz
Julie’s research works to comment on institutional influences on patterns of collaboration in producing research of interdisciplinary character. She specifically works to investigate the effects of institutional organizational processes on scientists’ knowledge production processes. For example, how does collaboration evolve in field of scientific practice? Using a combination of social network analysis, bibliomterics and computational social models (e.g. longitudinal actor-based network models such as ERGM), Additionally, she is working within the Semantically Mapping Science Project (http://www.sms-project.org/) which implements the use of Web data to assess science.
Research interests include: knowledge innovation in academic networks, dynamic cooperation techniques in arising collaboration networks, and ephemeral network structures
Mean-field approach for large social networks