A multi-graph spectral framework for mining multi-source anomalies

Jing Gao; Nan Du; Wei Fan; Deepak Turaga; Srinivasan Parthasarathy; Jiawei Han

doi:10.1007/978-1-4614-4457-2_9

A multi-graph spectral framework for mining multi-source anomalies

Jing Gao
, Nan Du
, Wei Fan
, Deepak Turaga
, Srinivasan Parthasarathy
, Jiawei Han

Department of Computer Science and Engineering

SUNY Buffalo
Huawei Noah Ark's Lab
IBM
University of Illinois at Urbana-Champaign

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

19 Scopus citations

Abstract

Anomaly detection refers to the task of detecting objects whose characteristics deviate significantly from the majority of the data [5]. It is widely used in a variety of domains, such as intrusion detection, fraud detection, and health monitoring. Today's information explosion generates significant challenges for anomaly detection when there exist many large, distributed data repositories consisting of a variety of data sources and formats.

Original language	English
Title of host publication	Graph Embedding for Pattern Analysis
Publisher	Springer New York
Pages	205-227
Number of pages	23
ISBN (Electronic)	9781461444572
ISBN (Print)	9781461444565
DOIs	https://doi.org/10.1007/978-1-4614-4457-2_9
State	Published - Jan 1 2013

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10.1007/978-1-4614-4457-2_9

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A multi-graph spectral framework for mining multi-source anomalies

Abstract

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