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Visual analysis of large-scale network anomalies

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3 Author(s)
Q. Liao ; Department of Computer Science, Central Michigan University, Mount Pleasant, MI, USA ; L. Shi ; C. Wang

The amount of information flowing across communication networks has rapidly increased. The highly dynamic and complex networks, represented as large graphs, make the analysis of such networks increasingly challenging. In this paper, we provide a brief overview of several useful visualization techniques for the analysis of spatiotemporal anomalies in large-scale networks. We make use of community-based similarity graphs (CSGs), temporal expansion model graphs (TEMGs), correlation graphs (CGs), high-dimension projection graphs (HDPGs), and topology-preserving compressed graphs (TPCGs). CSG is used to detect anomalies based on community membership changes rather than individual nodes and edges and therefore may be more tolerant to the highly dynamic nature of large networks. TEMG transforms network topologies into directed trees so that efficient search is more likely to be performed for anomalous changes in network behavior and routing topology in large dynamic networks. CG and HDPG are used to examine the complex relationship of data dimensions among graph nodes through transformation in a high-dimensional space. TPCG groups nodes with similar neighbor sets into mega-nodes, thus making graph visualization and analysis more scalable to large networks. All the methods target efficient large-graph anomaly visualization from different perspectives and together provide valuable insights.

Note: The Institute of Electrical and Electronics Engineers, Incorporated is distributing this Article with permission of the International Business Machines Corporation (IBM) who is the exclusive owner. The recipient of this Article may not assign, sublicense, lease, rent or otherwise transfer, reproduce, prepare derivative works, publicly display or perform, or distribute the Article.  

Published in:

IBM Journal of Research and Development  (Volume:57 ,  Issue: 3/4 )