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Discovering descriptive rules in relational dynamic graphs
2013
Intelligent Data Analysis
Graph mining methods have become quite popular and a timely challenge is to discover dynamic properties in evolving graphs or networks. We consider the so-called relational dynamic oriented graphs that can be encoded as n-ary relations with n 3 and thus represented by Boolean tensors. Two dimensions are used to encode the graph adjacency matrices and at least one other denotes time. We design the pattern domain of multi-dimensional association rules, i.e., non trivial extensions of the popular
doi:10.3233/ida-120567
fatcat:s4ck47vfqnczfmeq4kdjsrc63q