BLADYG: A Graph Processing Framework for Large Dynamic Graphs

TL;DR

BLADYG is a new block-centric graph processing framework designed to efficiently handle large, dynamic graphs, addressing scalability and dynamism issues with an implementation based on the Akka framework.

Contribution

This paper introduces BLADYG, a novel block-centric framework for processing large dynamic graphs, focusing on scalability and dynamism, with an implementation on Akka.

Findings

Demonstrates improved performance on large dynamic graphs

Addresses scalability challenges in block-centric graph processing

Provides an effective implementation on Akka framework

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.