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.
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|Titolo:||BLADYG: A novel block-centric framework for the analysis of large dynamic graphs|
|Autori:||Aridhi, Sabeur; Montresor, Alberto; Velegrakis, Yannis|
|Titolo del volume contenente il saggio:||HPGP 2016 - Proceedings of the ACM Workshop on High Performance Graph Processing, Co-located with HPDC 2016|
|Luogo di edizione:||New York|
|Casa editrice:||Association for Computing Machinery, Inc|
|Anno di pubblicazione:||2016|
|Codice identificativo Scopus:||2-s2.0-84978924716|
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