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Conference Papers Year : 2022

RTGEN : A Relative Temporal Graph GENerator

Abstract

Graph management systems are emerging as an efficient solution to store and query graph-oriented data. To assess the performance and compare such systems, practitioners often design benchmarks in which they use large scale graphs. However, such graphs either do not fit the scale requirements or are not publicly available. This has been the incentive of a number of graph generators which produce synthetic graphs whose characteristics mimic those of real-world graphs (degree distribution, community structure, diameter, etc.). Applications, however, require to deal with temporal graphs whose topology is in constant change. Although generating static graphs has been extensively studied in the literature, generating temporal graphs has received much less attention. In this work, we propose RTGEN a relative temporal graph generator that allows the generation of temporal graphs by controlling the evolution of the degree distribution. In particular, we propose to generate new graphs with a desired degree distribution out of existing ones while minimizing the efforts to transform our source graph to target. Our proposed relative graph generation method relies on optimal transport methods. We extend our method to also deal with the community structure of the generated graphs that is prevalent in a number of applications. Our generation model extends the concepts proposed in the Chung-Lu model with a temporal and community-aware support. We validate our generation procedure through experiments that prove the reliability of the generated graphs with the ground-truth parameters.
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hal-03609893 , version 1 (16-03-2022)

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  • HAL Id : hal-03609893 , version 1

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Maria Massri, Zoltan Miklos, Philippe Raipin, Pierre Meye. RTGEN : A Relative Temporal Graph GENerator. DATAPLAT workshop at the EDBT/ICDT 2022 Joint Conference, Mar 2022, Edinburgh, United Kingdom. ⟨hal-03609893⟩
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