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The process of network evolution describes changes in the behavior of a network structure. However, defining what is changing in large computer networks can be challenging especially when dealing with numerous nodes involved in large volumes of traffic flows over multiple periods of time. Therefore, studying an evolving computer network can be used to determine what exactly is changing in the network. Such changes in traffic can be defined in terms of sudden absence of key nodes or edges, or the addition of new nodes and edges to the network. These are micro level changes. This on the other hand may lead to changes at the macro level of the network such as changes in the density and diameter of the network that describe connectivity between nodes as well as flow of information within the network. Most importantly, observing a network’s behavior at different points in time can be used to determine the time when such changes occurred. We refer to such a changing network as a temporally evolving computer network.

In this project we develop an approach, to detect change, which utilizes strategic sampling to select central nodes and key subgraphs from networks over time. We also develop a multi-level approach that detects changes in the network by evaluating the behavior of central nodes over time. Additionally, our approach utilizes big data framework to process large graphs efficiently.