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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Discovering Relevant Cross-Graph Cliques in Dynamic Networks
verfasst von : Loïc Cerf, Tran Bao Nhan Nguyen, Jean-François Boulicaut
Erschienen in: Foundations of Intelligent Systems
Verlag: Springer Berlin Heidelberg
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Several algorithms, namely
CubeMiner
,
Trias
, and
Data-Peeler
, have been recently proposed to mine closed patterns in ternary relations. We consider here the specific context where a ternary relation denotes the value of a graph adjacency matrix at different timestamps. Then, 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, clique relevancy can be specified by means of a conjunction of constraints which can be efficiently exploited. The added-value of our strategy 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 domain knowledge on the considered city.