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Journal Articles Big Data Research Year : 2017

BLADYG: A Graph Processing Framework for Large Dynamic Graphs


Recently, distributed processing of large dynamic graphs has become very popular , especially in certain domains such as social network analysis, Web graph analysis and spatial network analysis. In this context, many distributed/parallel graph processing systems have been proposed, such as Pregel, PowerGraph, GraphLab, and Trinity. However, these systems deal only with static graphs and do not consider the issue of processing evolving and dynamic graphs. In this paper, we are considering the issues of scale and dynamism in the case of graph processing systems. We present BLADYG, a graph processing framework that addresses the issue of dynamism in large-scale graphs. We present an implementation of BLADYG on top of akka framework. We experimentally evaluate the performance of the proposed framework by applying it to problems such as distributed k-core decomposition and partitioning of large dynamic graphs. The experimental results show that the performance and scalability of BLADYG are satisfying for large-scale dynamic graphs.
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Dates and versions

hal-01577882 , version 1 (28-08-2017)



Sabeur Aridhi, Alberto Montresor, Yannis Velegrakis. BLADYG: A Graph Processing Framework for Large Dynamic Graphs. Big Data Research, 2017, ⟨10.1016/j.bdr.2017.05.003⟩. ⟨hal-01577882⟩
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