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Mining Constrained Cross-Graph Cliques in Dynamic Networks

Loïc Cerf 1 Tran Bao Nhan Nguyen 2 Jean-François Boulicaut 1
1 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 COMBINING - COMputational BIology and data miNING
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information, Inria Grenoble - Rhône-Alpes
Abstract : Three algorithms — CubeMiner, Trias, and Data-Peeler — have been recently proposed to mine closed patterns in ternary relations, i.e., a generalization of the so-called formal concept extraction from binary relations. In this paper, we consider the specific context where a ternary relation denotes the value of a graph adjacency matrix (i. e., a Vertices × Vertices matrix) at different timestamps. We discuss the constraint-based extraction of patterns in such dynamic graphs. We formalize the concept of δ-contiguous closed 3-clique and we discuss the availability of a complete algorithm for mining them. It is based on a specialization of the enumeration strategy implemented in Data-Peeler. Indeed, the relevant cliques are specified by means of a conjunction of constraints which can be efficiently exploited. The added-value of our strategy for computing constrained clique patterns is assessed on a real dataset about a public bicycle renting system. The raw data encode the relationships between the renting stations during one year. The extracted δ-contiguous closed 3-cliques are shown to be consistent with our knowledge on the considered city.
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Submitted on : Friday, October 14, 2016 - 2:48:20 PM
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  • HAL Id : hal-01381541, version 1


Loïc Cerf, Tran Bao Nhan Nguyen, Jean-François Boulicaut. Mining Constrained Cross-Graph Cliques in Dynamic Networks. Dzeroski, Goethals, Panov. Inductive Databases and Constraint-based Data Mining, Springer, pp.199-228, 2010. ⟨hal-01381541⟩



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