Title :
STUN: Spatio-Temporal Uncertain (Social) Networks
Author :
Chanhyun Kang ; Pugliese, Andrea ; Grant, James ; Subrahmanian, V.S.
Author_Institution :
Comput. Sci. Dept., Univ. of Maryland, College Park, MD, USA
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.
Keywords :
network theory (graphs); query languages; question answering (information retrieval); social networking (online); spatiotemporal phenomena; uncertainty handling; STUN; YouTube; index structure; query language; question answering system; social network; spatiotemporal constraint; spatiotemporal uncertain network; subgraph; uncertainty requirement; Educational institutions; Electronic mail; Facebook; Indexes; Knowledge based systems; Uncertainty;
Conference_Titel :
Advances in Social Networks Analysis and Mining (ASONAM), 2012 IEEE/ACM International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4673-2497-7
DOI :
10.1109/ASONAM.2012.93