This paper provides a comprehensive survey of distributed graph algorithms for large-scale graphs, addressing the challenges and solutions in distributed graph processing. The authors, from various institutions in China and Australia, analyze the inherent challenges such as parallelism, load balancing, communication overhead, and bandwidth limitations. They present an overview of existing solutions, including generic frameworks like Pregel and specialized models like Vertex-Centric, Edge-Centric, and Subgraph-Centric. The paper categorizes popular graph tasks into seven topics: Centrality, Community Detection, Similarity, Cohesive Subgraph, Traversal, Pattern Matching, and Covering. Each topic is analyzed in detail, highlighting the specific challenges and solutions. The authors also discuss current research trends and potential future opportunities, emphasizing the need for more efficient and effective distributed graph processing techniques. The paper concludes with a comprehensive graph that visualizes the connections between papers, topics, algorithms, solutions, and challenges, providing a detailed landscape of the field.This paper provides a comprehensive survey of distributed graph algorithms for large-scale graphs, addressing the challenges and solutions in distributed graph processing. The authors, from various institutions in China and Australia, analyze the inherent challenges such as parallelism, load balancing, communication overhead, and bandwidth limitations. They present an overview of existing solutions, including generic frameworks like Pregel and specialized models like Vertex-Centric, Edge-Centric, and Subgraph-Centric. The paper categorizes popular graph tasks into seven topics: Centrality, Community Detection, Similarity, Cohesive Subgraph, Traversal, Pattern Matching, and Covering. Each topic is analyzed in detail, highlighting the specific challenges and solutions. The authors also discuss current research trends and potential future opportunities, emphasizing the need for more efficient and effective distributed graph processing techniques. The paper concludes with a comprehensive graph that visualizes the connections between papers, topics, algorithms, solutions, and challenges, providing a detailed landscape of the field.