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A Distributed Framework for Large-Scale Time-Dependent Graph Analysis

Abstract : In the last few years, we have seen that many applications or computer problems are mobilized as a graph since this data structure gives a particular handling for some use cases such as social networks, bioinformatics, road networks and communication networks. Despite its importance, the graph processing remains a challenge when dealing with large graphs. In this context, several solutions and works have been proposed to support large graph processing and storage. Nevertheless, new needs are emerging to support the dynamism of the graph (Dynamic Graph) and properties variation of the graph during the time (temporal graph). In this paper, we first present the concepts of dynamic and temporal graphs. Secondly, we show some frameworks that treat static, dynamic and temporal graphs. Finally, we propose a new framework based on the limits of the frameworks study.
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Submitted on : Saturday, September 23, 2017 - 4:32:08 PM
Last modification on : Saturday, June 25, 2022 - 7:42:06 PM


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  • HAL Id : hal-01590675, version 2


Wissem Inoubli, Livia Almada, Ticiana L. Coelho da Silva, Gustavo Coutinho, Lucas Peres, et al.. A Distributed Framework for Large-Scale Time-Dependent Graph Analysis. ECML PKDD 2017 - TD-LSG 2017 : workshop Advances in Mining Large-Scale Time Dependent Graphs, Sep 2017, Skopje, Macedonia. pp.6. ⟨hal-01590675v2⟩



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