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Static Graphs consist of a fixed sequence of nodes and edges which does not change over time, hence lack in providing the information regarding evolution of the network. In contrast, Dynamic Graphs to a greater extent relate to real-life events and so provide complete information about the network evolution. That is why many researchers [1, 2, 3, 5, 6, 7, 8, 9 and 10] have developed interest in mining...
Mining of regular patterns in dynamic networks finds immense application in characterizing the local properties of the networks, like behaviour (friendship relation), event occurrence (football matches). They in then are used to predict their future trends. But if they do not entail weight and direction aspects of the dynamic network, there can be loss of several significant details, such as strength...
Existing Dynamic Graph mining algorithms focus typically on finding patterns in undirected, unweighted and weighted dynamic networks ignoring the fact that some of them could be directed also. In this paper, we focus on finding regular evolution patterns in edges, outdegree and indegree of all the nodes, featuring consecutively at fixed time intervals during the growth of an unweighted and directed...
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