Contractor presents at a National Cancer Institute Webinar
Contractor delivered a webinar for the National Cancer Institute, Behavioral Research Program, Science of Research and Technology Branch, Speaker Series on “Disruptive Research Approaches from Academia, Government, and Industry” on March 28, 2013.
Hank Green to present in the Sonic Speaker Series
SONIC lab is proud to welcome Hank Green, who will present a talk on Thursday, April 11, 2013 (04:00-05:15pm) in the Stamler Conference Room (680 N Lakeshore Drive, 14th floor), Chicago Campus. All are welcome to attend.
About The Talk: Applying Network Science: An Evolving Perspective
While opinion leader and other peerbased interventions are firmly established in public health research contexts where we seek to impact individuals’ behaviors, developments in network analytic methods have led to changes in the way we understand peer influence and selection processes and in the way we can apply network studies for intervention development. I will present my framework for understanding networkbased interventions and describe how that framework has evolved in light of new network statistical approaches. I will illustrate this evolutionary process with examples of crosssectional and longitudinal studies that link individual behaviors and attitudes to network structure and composition. I conclude with some discussions of future directions in research and in application.
About Hank Green
Harold D. Green, Jr. (Hank) is a Senior Behavioral Scientist at the RAND Corporation in Santa Monica, where he is the coordinator of the RAND Applied Network Analysis Research Group. Hank uses network analyses to understand the social and cultural determinants of health. In addition to his applied work, Hank is active in designing and implementing specialized software for the collection of longitudinal personal network data via the Internet. He holds a Ph.D. in Anthropology from the University of Florida and is an Alumnus of the University of Illinois Training Grant in Quantitative Psychology, of the National Center for Supercomputing Applications Center for the Humanities, Arts and Social Sciences, and of the Science of Networks in Communities Research Group.
Network Science: Interactive Textbook
An incredible map of which countries e-mail each other, and why
Data Crunchers Now the Cool Kids on Campus – WSJ.com
CALL FOR PROPOSALS: 4TH ANNUAL DOCTORAL CONSORTIUM OF THE COMMUNICATION AND TECHNOLOGY DIVISION OF THE INTERNATIONAL COMMUNICATION ASSOCIATION ***APPLICATION DEADLINE EXTENDED TO MARCH 15, 2013!
Jana Diesner to present in the Sonic Speaker Series
SONIC lab is proud to welcome Jana Diesner, who will present a talk on Thursday, Mar 07, 2013 (12:00-01:15pm) in Frances Searle Building Room 1.483 on Northwestern’s Evanston Campus. All are welcome to attend.
About The Talk: From Words to Networks: Relevance of methodological choices for real-world applications
Coding texts as socio-technical networks – a process also known as relation extraction – can be used to collect network data on hard-to-access groups and organizations. This process requires people to choose appropriate methods and parameter settings. The impact of these choices on the resulting data and findings can be strong, but is hardly understood. I discuss our findings from addressing this problem:
We applied four common relation extraction methods – from fairly qualitative to fully automated (including probabilistic, machine-learning based techniques) – to large-scale, open-source corpora from the business, science and geopolitical domain, and compared the retrieved networks. I will report on common agreements and disagreements about network structure and behavior depending on the considered methods, and show how these methods can be combined to gain a more robust and comprehensive understanding of a network.
Another factor limiting the reliability of relation extraction methods is the propagation of errors throughout multi-step analysis procedures and pipelines. I will present our findings from a series of empirical experiments that we conducted to find answers to the following question: How much variation in network structure and properties is due to the error rates of the involved sub-routines? Does increasing the accuracy of these techniques actually matter for network analysis results?
About Jana Diesner
Jana Diesner is an Assistant Professor at the iSchool (a.k.a. Graduate School of Library and Information Science) at the University of Illinois at Urbana-Champaign. She earned her PhD from Carnegie Mellon University, School of Computer Science. Jana conducts research at the nexus of network science, natural language processing and machine learning. With her work, she aims to advance the understanding and computational analysis of the interplay and co-evolution of information and socio-technical networks. She develops, analyzes and applies methods and technologies for extracting information about networks from text data and considering the substance of information for network analysis. In her empirical work, she studies networks from the business, science and geopolitical domain. She is particularly interested in covert information and covert networks. For more information see http://people.lis.illinois.