Glossary
- Event Detection:
-
Identifying patterns of interest in large temporal datasets
- Spatial Scan Statistic:
-
A method for identifying hotspots in spatial data, widely used in epidemiology and biosurveillance
- Scoring Function:
-
An objective function that measures the anomalousness of a subset of data
- LTSS:
-
Linear-time subset scanning
- Time to Detect:
-
Evaluation metric; time delay before detecting an event
- Overlap:
-
Evaluation metric; accuracy of detected subsets of data
- Detection Power:
-
Evaluation metric; proportion of detected events
Definition
GraphScan is a novel method for detecting arbitrarily shaped connected clusters in graph or network data. Given a graph structure, data observed at each node of the graph, and a score function defining the anomalousness of a set of nodes, GraphScan can efficiently and exactly identify the most anomalous (highest-scoring) connected subgraph....
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akoglu L, McGlohon M, Faloutsos C (2010) Oddball: spotting anomalies in weighted graphs. In: PAKDD (2), Hyderabad, pp 410–421
Chakrabarti D (2004) Autopart: parameter-free graph partitioning and outlier detection. In: PKDD, Pisa, pp 112–124
Kulldorff M (1997) A spatial scan statistic. Comput Stat 26(6):1481–1496 MATHMathSciNet
Neill DB (2012) Fast subset scan for spatial pattern detection. J R Stat Soc B 74(2):337–360 MathSciNet
Neill DB, Moore AW, Sabhnani MR, Daniel K (2005) Detection of emerging space-time clusters. In: Proceedings of the 11th ACM SIGKDD international conference on knowledge discovery and data mining, Chicago
Patil GP, Taillie C (2004) Upper level set scan statistic for detecting arbitrarily shaped hotspots. Environ Ecol Stat 11:183–197 MathSciNet
Somanchi S, Neill DB (2011) Fast graph structure learning from unlabeled data for outbreak detection. Emerg Health Threats J 4:11017
Somanchi S, Neill DB (2012) Fast graph structure learning from unlabeled data for event detection (Submitted)
Speakman S, Neill DB (2010) Fast graph scan for scalable detection of arbitrary connected clusters. In: Proceedings of the 2009 international society for disease surveillance annual conference, Miami Beach
Speakman S, McFowland E, Neill DB (2012) Scalable detection of anomalous patterns with connectivity constraints (Submitted)
Tango T, Takahashi K (2005) A flexibly shaped spatial scan statistic for detecting clusters. Int J Health Geogr 4:11
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2018 Springer Science+Business Media LLC, part of Springer Nature
About this entry
Cite this entry
Speakman, S., Somanchi, S., McFowland III, E., Neill, D.B. (2018). Disease Surveillance: Case Study. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-7131-2_283
Download citation
DOI: https://doi.org/10.1007/978-1-4939-7131-2_283
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4939-7130-5
Online ISBN: 978-1-4939-7131-2
eBook Packages: Computer ScienceReference Module Computer Science and Engineering