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Discovering Descriptive Rules in Relational Dynamic Graphs

Thi Kim Ngan Nguyen 1 Loic Cerf Marc Plantevit 1 Jean-François Boulicaut 1
1 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : 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 association rules that may involve subsets of any dimensions in their antecedents and their consequents. First, we design new objective interestingness measures for such rules and it leads to different approaches for measuring the rule confidence. Second, we must compute collections of a priori interesting rules. It is considered here as a post-processing of the closed patterns that can be extracted efficiently from Boolean tensors. We propose optimizations to support both rule extraction scalability and non redundancy. We illustrate the added-value of this new data mining task to discover patterns from a real-life relational dynamic graph.
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Submitted on : Thursday, August 4, 2016 - 3:39:35 PM
Last modification on : Tuesday, June 1, 2021 - 2:08:09 PM


  • HAL Id : hal-01351698, version 1


Thi Kim Ngan Nguyen, Loic Cerf, Marc Plantevit, Jean-François Boulicaut. Discovering Descriptive Rules in Relational Dynamic Graphs. Intelligent Data Analysis, IOS Press, 2013, 1, 17, pp.49-69. ⟨hal-01351698⟩



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