Social Communities Knowledge Discovery: Approaches applied to clinical study
dc.contributor.author | Chandrasekar, Prashant | en |
dc.date.accessioned | 2017-06-28T15:14:49Z | en |
dc.date.available | 2017-06-28T15:14:49Z | en |
dc.date.issued | 2017-05 | en |
dc.description.abstract | In recent efforts being conducted by the Social Interactome team, to validate hypotheses of the study, we have worked to make sense of the data that has been collected during two 16-week experiments and three Amazon Mechanical Turk deployments. The complexity in the data has made it challenging to discover insights/patterns. The goal of the semester was to explore newer methods to analyze the data. Through such discovery, we can test/validate hypotheses about the data, that would provide a direction for our contextual inquiry to predict attributes and behavior of participants in the study. The report and slides highlight two possible approaches that employ statistical relational learning for structure learning and network classification. Related files include data and software used during this study; results are given from the analyses undertaken. | en |
dc.description.notes | Submission contains following files: - SocialCommunitiesFiles.zip: Zipped folder containing the software/code for execution. Contents are described in the readme file. - SocialCommunitiesReport.docx: Final report of the class project outlining the goals and describing outcomes. - SocialCommunitiesReport.pdf: PDF version of the above docx file - SocialCommunitiesReport.pptx: Presentation of the class project - SocialCommunitiesReport.pdf: PDF version of the above pptx file | en |
dc.description.sponsorship | NIH Grant 1R01DA039456-01: The Social Interactome of Recovery: Social Media as Therapy Development | en |
dc.identifier.uri | http://hdl.handle.net/10919/78272 | en |
dc.language.iso | en_US | en |
dc.publisher | Virginia Tech | en |
dc.rights | Creative Commons Attribution 3.0 United States | en |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | en |
dc.subject | Markov Networks | en |
dc.subject | Prediction | en |
dc.subject | Network Classification | en |
dc.subject | Statistical Relational Learning | en |
dc.subject | Markov Logic Networks | en |
dc.title | Social Communities Knowledge Discovery: Approaches applied to clinical study | en |
dc.type | Dataset | en |
dc.type | Presentation | en |
dc.type | Report | en |
dc.type | Software | en |
Files
Original bundle
1 - 5 of 5
Loading...
- Name:
- SocialCommunitiesReport.pdf
- Size:
- 343.73 KB
- Format:
- Adobe Portable Document Format
Loading...
- Name:
- SocialCommunitiesPresentation.pdf
- Size:
- 662.86 KB
- Format:
- Adobe Portable Document Format
License bundle
1 - 1 of 1
- Name:
- license.txt
- Size:
- 1.5 KB
- Format:
- Item-specific license agreed upon to submission
- Description: