Intelligent network management using graph differential anomaly visualization

Qi Liao, Aaron Striegel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

Managing large-scale networks involving users and applications is challenging due to the complexity and dynamic nature of the heterogeneous graphs. How to quickly identify the meaningful changes and hidden anomalous activities in the spatiotemporally dynamic network graphs is essential in many aspects of network management, such as security, performance and troubleshooting. In this paper, we explore the viability and efficacy of a novel graph differential anomaly visualization (DAV) model in the area of network management. Our approach combines algorithmic graph analysis methods and visualization technologies by taking advantages from both computer and human intelligence. We focus on DAV at various levels, i.e., nodes, links and communities. Specifically, a novel community-based DAV scheme is proposed that can help understand the managed networks with a right balance of granularity and complexity. More importantly, the community-based DAV algorithm is less susceptible to network dynamics and high churn. The developed visual analytic tool can not only detect but more importantly find the root causes of anomalies in a time efficient manner.

Original languageEnglish
Title of host publicationProceedings of the 2012 IEEE Network Operations and Management Symposium, NOMS 2012
Pages1008-1014
Number of pages7
DOIs
StatePublished - 2012
Event2012 IEEE Network Operations and Management Symposium, NOMS 2012 - Maui, HI, United States
Duration: Apr 16 2012Apr 20 2012

Publication series

NameProceedings of the 2012 IEEE Network Operations and Management Symposium, NOMS 2012

Conference

Conference2012 IEEE Network Operations and Management Symposium, NOMS 2012
Country/TerritoryUnited States
CityMaui, HI
Period04/16/1204/20/12

Fingerprint

Dive into the research topics of 'Intelligent network management using graph differential anomaly visualization'. Together they form a unique fingerprint.

Cite this