We study group-summarization of probabilistic graphs that naturally arise in social
networks, semistructured data, and other applications. Our proposed framework
groups the nodes and edges of the graph based on a user selected set of node attributes.
We present methods to compute useful graph aggregates without the need
to create all of the possible graph-instances of the original probabilistic graph. Also,
we present an algorithm for graph summarization based on pure relational (SQL)
technology. We analyze our algorithm and practically evaluate its efficiency using
an extended Epinions dataset as well as synthetic datasets. The experimental results
show the scalability of our algorithm and its efficiency in producing highly compressed
summary graphs in reasonable time. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/4403 |
| Date | 03 January 2013 |
| Creators | Hassanlou, Nasrin |
| Contributors | Thomo, Alex |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web |
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