  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.
