Sciweavers

Free Online Productivity Tools i2Speak i2Symbol i2OCR iTex2Img iWeb2Print iWeb2Shot i2Type iPdf2Split iPdf2Merge i2Bopomofo i2Arabic i2Style i2Image i2PDF iLatex2Rtf Sci2ools

20

CSDA
2010

favoriteEmaildiscussreport

124views more CSDA 2010»

Statistical inference on attributed random graphs: Fusion of graph features and content

13 years 7 months ago

Statistical inference on attributed random graphs: Fusion of graph features and content

Download web.jhu.edu

Abstract: Fusion of information from graph features and content can provide superior inference for an anomaly detection task, compared to the corresponding content-only or graph feature-only statistics. In this paper, we design and execute an experiment on a time series of attributed graphs extracted from the Enron email corpus which demonstrates the benefit of fusion. The experiment is based on injecting a controlled anomaly into the real data and measuring its detectability. 1

John Grothendieck, Carey E. Priebe, Allen L. Gorin

Real-time Traffic

Anomaly Detection | CSDA 2010 | Enron Email Corpus | Graph Feature-only Statistics |

claim paper

Related Content

» Random attributed graphs for statistical inference from content and context

» Energy Efficient Routing for Statistical Inference of Markov Random Fields

» Minimum Cost Data Aggregation with Localized Processing for Statistical Inference

» Graph based multimodality learning

» Bridging the Semantic Gap Between Image Contents and Tags

» Structural Inference of Hierarchies in Networks

» Perceptual Scale Space and its Applications

» Graftinglight fast incremental feature selection and structure learning of Markov random f...

» Supervised Random Walks Predicting and Recommending Links in Social Networks

Post Info
More Details (n/a)

Added	09 Dec 2010
Updated	09 Dec 2010
Type	Journal
Year	2010
Where	CSDA
Authors	John Grothendieck, Carey E. Priebe, Allen L. Gorin

Comments (0)