A Survey of Distributed Graph Algorithms on Massive Graphs

TL;DR

This survey comprehensively reviews the challenges, solutions, and research trends in distributed graph algorithms for large-scale graphs, highlighting key issues like parallelism, load balancing, and communication overhead.

Contribution

It provides an extensive overview of current challenges, solutions, and future directions in distributed graph processing frameworks and algorithms.

Findings

Identifies key challenges such as parallelism and communication overhead.

Summarizes existing solutions and strategies for distributed graph processing.

Highlights emerging research trends and future opportunities.

Abstract

Distributed processing of large-scale graph data has many practical applications and has been widely studied. In recent years, a lot of distributed graph processing frameworks and algorithms have been proposed. While many efforts have been devoted to analyzing these, with most analyzing them based on programming models, less research focuses on understanding their challenges in distributed environments. Applying graph tasks to distributed environments is not easy, often facing numerous challenges through our analysis, including parallelism, load balancing, communication overhead, and bandwidth. In this paper, we provide an extensive overview of the current state-of-the-art in this field by outlining the challenges and solutions of distributed graph algorithms. We first conduct a systematic analysis of the inherent challenges in distributed graph processing, followed by presenting an overview of existing general solutions. Subsequently, we survey the challenges highlighted in recent distributed graph processing papers and the strategies adopted to address them. Finally, we discuss the current research trends and identify potential future opportunities.