Name
Abstract | ||
|---|---|---|
In this paper,
we consider the problem of social networks whose edges may be characterized with uncertainty, space, and time. We propose a model called spatio-temporal uncertain networks (STUN) to formally define such networks, and then we propose the concept of STUN subgraph matching (or SM) queries. We develop a hierarchical index structure to answer SM queries to STUN databases and show that the index supports answering very complex queries over 1M+ edge networks in under a second. We also introduce the class of STUNRank queries in which we characterize the importance of vertices in STUN databases, taking space, time, and uncertainty into account. We show query-aware and query-unaware versions of STUNRank as well as alternative ways of defining it. We report on an experimental evaluation of STUNRank showing that it performs well on real world networks. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s13278-014-0156-x | Social Netw. Analys. Mining |
Keywords | Field | DocType |
Social networks, Subgraph matching, Uncertainty, Spatio-temporal data | Data mining,Social network,Vertex (geometry),Theoretical computer science,STUN,Mathematics | Journal |
Volume | Issue | ISSN |
4 | 1 | 1869-5469 |
Citations | PageRank | References |
4 | 0.41 | 41 |
Authors | ||
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 |