1 / 5

SNAI.1: DATA

SNAI.1: DATA. Cyberinfrastructure for Social Network Analysis David Knoke, Gavin La Rowe, Matt Arrot, Cristina Beldica, Danyel Fisher, Kirby Vandivort, Hank Green. Data Collection. Longitudinal: 24/7/365 across all behavior traits

chandra-vaikunth
Download Presentation

SNAI.1: DATA

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. SNAI.1: DATA Cyberinfrastructure for Social Network Analysis David Knoke, Gavin La Rowe, Matt Arrot, Cristina Beldica, Danyel Fisher, Kirby Vandivort, Hank Green

  2. Data Collection • Longitudinal: 24/7/365 across all behavior traits • Continuous (tagging by cell phones or other electronics): measure proximity and maybe behavior in some public spaces • Emergence of new content, new relationships: emerging or evolving structure and flows.

  3. Scale of Social Network Data • Scale up SN software to handle larger data sets • How do we mine large sets of data across broad categories of content, and how do they relate (how are actors interconnected?). • Can this be automated with high reliability? • Integrating different levels of data sets and integrating social with nonsocial data: combining geophysical and biosystem data with social interactions • Richer data may lead to revival of human ecology as a viable field?

  4. Issues for Data Analysts • Storage and Computation time for large data sets Nn • Modular approaches for data access and computation • Storage issues • Independent users accessing same data sets

  5. Ethics/Privacy • Capable of merging sets of data from multiple sources (credit, information seeking, public behavior, clothes-penetrating radar) to track and identify individuals • Public behaviors can be observed without consent • Potential for image recognition in public places • Does this lead to Big Brother? • DARPA tried to do this already.

More Related