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Mining Constrained Cross-Graph Cliques in Dynamic Networks

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Inductive Databases and Constraint-Based Data Mining

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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Correspondence to Loïc Cerf .

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Cerf, L., Nguyen, B., Boulicaut, JF. (2010). Mining Constrained Cross-Graph Cliques in Dynamic Networks. In: Džeroski, S., Goethals, B., Panov, P. (eds) Inductive Databases and Constraint-Based Data Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7738-0_9

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  • DOI: https://doi.org/10.1007/978-1-4419-7738-0_9

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