BLADYG: A novel block-centric framework for the analysis of large dynamic graphs

Sabeur Aridhi, Alberto Montresor, Yannis Velegrakis

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

3 Citations (Scopus)

Abstract

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, GraphLab, and Trinity. These systems can be divided into two categories: (1) vertex-centric and (2) block-centric approaches. In vertex-centric approaches, each vertex corresponds to a process, and message are exchanged among vertices. In block-centric approaches, the unit of computation is a block, a connected subgraph of the graph, and message exchanges occur among blocks. In this paper, we are considering the issues of scale and dynamism in the case of block-centric approaches. We present BLADYG, a block-centric 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.

Original languageEnglish
Title of host publicationHPGP 2016 - Proceedings of the ACM Workshop on High Performance Graph Processing, Co-located with HPDC 2016
PublisherACM
Pages39-42
Number of pages4
ISBN (Electronic)9781450343503
DOIs
Publication statusPublished - 31 May 2016
MoE publication typeA4 Article in a conference publication
EventACM Workshop on High Performance Graph Processing - Kyoto, Japan
Duration: 31 May 20164 Jun 2016

Workshop

WorkshopACM Workshop on High Performance Graph Processing
Abbreviated titleHPGP
CountryJapan
CityKyoto
Period31/05/201604/06/2016

Keywords

  • AKKA framework
  • Distributed graph processing
  • Dynamic graphs

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    Aridhi, S., Montresor, A., & Velegrakis, Y. (2016). BLADYG: A novel block-centric framework for the analysis of large dynamic graphs. In HPGP 2016 - Proceedings of the ACM Workshop on High Performance Graph Processing, Co-located with HPDC 2016 (pp. 39-42). ACM. https://doi.org/10.1145/2915516.2915525