Name
Abstract | ||
|---|---|---|
STUN is an extension of social networks in which the edges are characterized by spatio-temporal annotations, as well as uncertainty allowing us to express not only relationships between vertices, but when and where these relationships were true, and how certain we are that the relationships hold. We propose a STUN query language that consists of sub graphs with spatio-temporal constraints and uncertainty requirements. We then develop an index structure to store STUN graphs, together with an algorithm to answer such queries. We describe experiments with a real-world YouTube social network data set and show that our algorithm performs well on graphs with over a million edges. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ASONAM.2012.93 | ASONAM |
Keywords | Field | DocType |
spatio-temporal uncertain,stun query language,youtube,social network,index structure,question answering system,spatiotemporal uncertain network,query languages,spatiotemporal constraint,spatiotemporal phenomena,subgraph,sub graph,real-world youtube social network,spatio-temporal annotation,uncertainty requirement,spatio-temporal constraint,uncertainty handling,million edge,query language,social networking (online),network theory (graphs),stun,question answering (information retrieval),stun graph,uncertainty,knowledge based systems,indexes | Graph,Data mining,Query language,Social network,Information retrieval,Vertex (geometry),Computer science,Knowledge-based systems,STUN,Artificial intelligence,Uncertainty handling,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4673-2497-7 | 3 | 0.37 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chanhyun Kang | 1 | 56 | 4.10 |
| Andrea Pugliese | 2 | 400 | 22.07 |
| John Grant | 3 | 763 | 237.04 |
| V. S. Subrahmanian | 4 | 6864 | 1053.38 |