Abstract

Although they clearly exist, affinities among individuals are not all easily identified. Yet, they offer unique opportunities to discover new social networks, strengthen ties among individuals, and provide recommendations. We propose the idea of Implicit Affinity Networks (IANs) to build, visualize, and track affinities among groups of individuals. IANs are simple, interactive graphical representations that users may navigate to uncover interesting patterns. This thesis describes a system supporting the construction of IANs and evaluates it in the context of family history and online communities.

Degree

MS

College and Department

Physical and Mathematical Sciences; Computer Science

Rights

http://lib.byu.edu/about/copyright/

Date Submitted

2007-01-05

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd1682

Keywords

communities, social network analysis, collaboration, community evolution, affinities, family history, business intelligence, recommendation systems, computer science

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