Probabilistic graph summarization
| dc.contributor.author | Hassanlou, Nasrin | |
| dc.contributor.supervisor | Thomo, Alex | |
| dc.date.accessioned | 2013-01-03T21:19:38Z | |
| dc.date.available | 2013-01-03T21:19:38Z | |
| dc.date.copyright | 2012 | en_US |
| dc.date.issued | 2013-01-03 | |
| dc.degree.department | Dept. of Computer Science | en_US |
| dc.degree.level | Master of Science M.Sc. | en_US |
| dc.description.abstract | 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. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/4403 | |
| dc.language | English | eng |
| dc.language.iso | en | en_US |
| dc.rights.temp | Available to the World Wide Web | en_US |
| dc.subject | graphs | en_US |
| dc.subject | users | en_US |
| dc.subject | algorithms | en_US |
| dc.title | Probabilistic graph summarization | en_US |
| dc.type | Thesis | en_US |