CyberInfrastructure for Network Analysis
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Transcript of CyberInfrastructure for Network Analysis
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CyberInfrastructure for
Network Analysis
•Importance of, contributions by network analysis•Transformation of NA•Support needed for NA
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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
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Transformation of NANetwork 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
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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
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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
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NA Technology• User Interfaces
– Facilitate/teach Analysis Process– History keeping/saving– Multilevel interfaces to address varying user
needs and abilities
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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
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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
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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 transparencyHow can we study private things while retaining confidentiality?
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• 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