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A Block-Based Edge Partitioning for Random Walks Algorithms over Large Social Graphs

Yifan Li 1 Camelia Constantin 1 Cedric Du Mouza 2 
1 BD - Bases de Données
LIP6 - Laboratoire d'Informatique de Paris 6
2 CEDRIC - ISID - CEDRIC. Ingénierie des Systèmes d'Information et de Décision
CEDRIC - Centre d'études et de recherche en informatique et communications
Abstract : Recent results [5, 9, 25] prove that edge partitioning approaches (also known as vertex-cut) outperform vertex partitioning (edge-cut) approaches for computations on large and skewed graphs like social networks. These vertex-cut approaches generally avoid unbalanced computation due to the power-law degree distribution problem. However, these methods, like evenly random assigning [25] or greedy assignment strategy [9], are generic and do not consider any computation pattern for specific graph algorithm. We propose in this paper a vertex-cut partitioning dedicated to random walks algorithms which takes advantage of graph topologi-cal properties. It relies on a blocks approach which captures local communities. Our split and merge algorithms allow to achieve load balancing of the workers and to maintain it dynamically. Our experiments illustrate the benefit of our partitioning since it significantly reduce the communication cost when performing random walks-based algorithms compared with existing approaches.
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Contributor : Yifan LI Connect in order to contact the contributor
Submitted on : Monday, November 21, 2016 - 5:05:37 PM
Last modification on : Friday, August 5, 2022 - 2:54:00 PM
Long-term archiving on: : Thursday, March 16, 2017 - 5:56:46 PM


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  • HAL Id : hal-01398195, version 1


Yifan Li, Camelia Constantin, Cedric Du Mouza. A Block-Based Edge Partitioning for Random Walks Algorithms over Large Social Graphs. 32ème Conférence sur la Gestion de Données - BDA2016, Nov 2016, Poitiers, France. ⟨hal-01398195⟩



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