Name
Abstract | ||
|---|---|---|
Detecting “hotspots” and “anomalies” is a recurring problem with a wide range of applications, such as social network analysis, epidemiology, finance, and biosurveillance, among others. Networks are a common abstraction in these applications for representing complex relationships. Typically, these networks are dynamic-, i.e., they evolve over time. A number of methods have been proposed for anomal... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/JPROC.2018.2813311 | Proceedings of the IEEE |
Keywords | DocType | Volume |
Anomaly detection,Microsoft Windows,Social network services,Sensors,Steiner trees,Computer science,Task analysis,Graph theory,Approximation algorithms,Graphical models | Journal | 106 |
Issue | ISSN | Citations |
5 | 0018-9219 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jose Cadena | 1 | 98 | 7.53 |
| Feng Chen | 2 | 451 | 48.47 |
| Anil Kumar S. Vullikanti | 3 | 1135 | 98.30 |