Namayanja, Josephine M.Janeja, Vandana P.2019-04-172019-04-172019-04Josephine M. Namayanja, Vandana P. Janeja, Change Detection in Large Evolving Networks, International Journal of Data Warehousing and Mining, Volume 15 , Issue 2 ,April-June 2019, DOI: 10.4018/IJDWM.2019040104https://doi.org/10.4018/IJDWM.2019040104http://hdl.handle.net/11603/13444This article presents a novel technique for the detection of change in massive evolving communication networks. This approach utilizes a novel hybrid sampling methodology to select central nodes and key subgraphs from networks over time. The objective is to select and utilize a much smaller targeted sample of the network, represented as a graph, without loss of any knowledge derived from graph properties as compared to the entire massive graph. This article uses the targeted samples to detect micro- and macro-level changes in the network. This approach can be potentially useful in the domain of cybersecurity where this article highlights the importance of graph sampling and multi-level change detection in identifying network changes that may be difficult to detect on a larger scale. This article therefore presents a means to audit large networks to establish continuous awareness of network behavior.18 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.© IGI GlobalBig Datacentral nodescyber threatshybrid samplingkey subgraphsmacro-level changesmicro-level changestemporal binningUMBC High Performance Computing Facility (HPCF)Text