Implicit Affinity Networks (IAN)
From DML
Due to the enormous amount of information available about individuals, it is increasingly challenging to exploit all of the inherent similarities, or affinities, that tie them together. 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 (IAN) to facilitate knowledge assimilation and uncover relationships 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.
IAN (Community Website) - This evolving website, currently BETA, serves as the environment for analyzing and presenting relevant IANs. Community evolution occurs across multiple dimensions; there exists a dynamic set of members each having a dynamic set of attributes (each having a dynamic set of values).
Contents |
[edit] Researchers
Matthew Smith, Christophe Giraud-Carrier, Nathan Purser, Brock Judkins
[edit] Sample IAN
This is a snapshot taken from October 2006 of the "research interests" IAN from http://dml.cs.byu.edu/IAN.
[edit] Publications
Smith, M., Giraud-Carrier, C. and Judkins, B. (2007). Implicit Affinity Networks. In Proceedings of the Seventeenth Annual Workshop on Information Technologies and Systems, 1-6.
Smith, M. (2007). Implicit Affinity Networks. Brigham Young University.
[edit] Related Topics
Social Network Analysis, Social Network Modeling, Social Influence / Viral Marketing, Social Bookmarking by Tagging, Collaboration, Communities / Blogging / Web 2.0, Social Capital
