1. CyberInfrastructure for Network Analysis Importance of, contributions by network analysis Transformation of NA Support needed for NA

2. Contributions by NA Grand scientific and societal challenges for which contributions by NA are essential, e.g.: Epidemiology Social influences in health-related behavior, substance (ab)use Distributed governance Politics – citizens opposition Organizational analysis Conflicts between groups within societies

3. Transformation of NA Network Analysis is currently transforming itself into a bigger (???) science: Data * ways (automated) of collecting them, in addition to manually collected data * size: complexity & number of nodes Analysis * computing * statistical modeling (beyond case studies) * visualization Multi (inter, trans?) disciplinarity

4. What is needed to fulfill the promises The promises of NA can be fulfilled only if there is strong extra support. Cyberinfrastructure: NA Technology: data collection, data availability, analysis, dissemination Support for multi-stranded collaboration (disciplines, techniques, research questions) This must be facilitated by CI, but also includes education, dissemination, incentive structures CI needs to handle diversity

5. NA Technology Extraction of network data –from text, photos, videos, logs, processes, web –Data cleaning, entity resolution –To Create better metadata (e.g. with history) –Links between data sets / papers / methods / … Dealing with Huge / Complex networks –Modeling, Approximation –New visualization and interaction techniques Temporal Analysis (including real time) Interoperability –Data and software level Social Engineering – setting experiments in CI – simulated worlds

6. NA Technology User Interfaces –Facilitate/teach Analysis Process –History keeping/saving –Multilevel interfaces to address varying user needs and abilities

7. Community support Grand challenge Map of SNA community Facilitate communication between and within disciplines (workshops, textbooks, web + paper tutorials) + many “CI-Generic tools” –Query-able Digital Library of paper ref., datasets, tools, people –Archive –+ lots of things

8. Evaluation Guidelines about what to use when User studies for evaluation of components Longitudinal studies (e.g. of the CI itself) Caution: standardization, monopolies reduce diversity

9. Open Questions For which ends do we need standards, which, how? Middelware. Centralize or not? Control must be loose! Is there a CI curator? Open source? Commercial vs. freeware? Ethics, privacy (note: some officials have more info than researchers!) We need new rules/norms for working with private info. Two-way transparency How can we study private things while retaining confidentiality?

10. What is the community? What are the communities? (note: cross-fertilization with system biologists) Note: small data sets remain important Important contributions by social theories/theorists to NA & this work Bias inherent in automated data collection Public dissemination of results also to general public and policy makers