Synthetic Graph Generation from Finely-Tuned Temporal Constraints

Abstract : Large-scale graphs are at the core of a plethora of modern applications such as social networks, transportation networks, or the Semantic Web. Such graphs are naturally evolving over time, which makes particularly challenging graph processing tasks e.g., graph mining. To be able to realize rigorous empirical evaluations of research ideas, the graph processing community needs finely-tuned generators of synthetic time-evolving graphs, which are particularly useful whenever real-world graphs are unavailable for public use. The goal of this paper is to report on an ongoing project that aims at generating synthetic time-evolving graphs satisfying finely-tuned temporal constraints specified by the user.
Type de document :
Communication dans un congrès
TDLSG 2017 (Advances in Mining Large-Scale Time-Dependent Graphs). Collocated with ECML/PKDD 2017, Sep 2017, Skopje, Macedonia. 〈http://tdlsg-ecmlpkdd17.isima.fr/TDLSG-ECMLPKDD_2017_paper_1.pdf〉
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https://hal.inria.fr/hal-01591711
Contributeur : Radu Ciucanu <>
Soumis le : jeudi 21 septembre 2017 - 19:20:21
Dernière modification le : lundi 5 février 2018 - 09:24:56

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  • HAL Id : hal-01591711, version 1

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Karim Alami, Radu Ciucanu, Engelbert Mephu Nguifo. Synthetic Graph Generation from Finely-Tuned Temporal Constraints. TDLSG 2017 (Advances in Mining Large-Scale Time-Dependent Graphs). Collocated with ECML/PKDD 2017, Sep 2017, Skopje, Macedonia. 〈http://tdlsg-ecmlpkdd17.isima.fr/TDLSG-ECMLPKDD_2017_paper_1.pdf〉. 〈hal-01591711〉

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