This paper presents a comprehensive survey of distributed graph algorithms on massive graphs, summarizing the challenges and solutions in this field. The paper highlights four main challenges in distributed graph processing: parallelism, load balance, communication overhead, and bandwidth. These challenges are addressed through various solutions, including parallel looping, message passing, and broadcasting. The paper also discusses the different algorithmic topics in distributed graph processing, such as centrality, community detection, similarity, cohesive subgraph, traversal, pattern matching, and covering. The paper provides an overview of existing distributed graph processing frameworks and systems, including Pregel, Giraph, GraphX, and GraphScope. It also discusses the current research trends and potential future opportunities in this field. The paper concludes with a detailed analysis of the challenges and solutions in each algorithmic topic, providing insights into the potential directions for future research. The paper also presents a comprehensive graph that encapsulates the surveyed material, mapping out the intricate connections among papers, topics, algorithms, solutions, and challenges. This graph provides a visual narrative of the landscape of distributed graph algorithms on massive graphs.This paper presents a comprehensive survey of distributed graph algorithms on massive graphs, summarizing the challenges and solutions in this field. The paper highlights four main challenges in distributed graph processing: parallelism, load balance, communication overhead, and bandwidth. These challenges are addressed through various solutions, including parallel looping, message passing, and broadcasting. The paper also discusses the different algorithmic topics in distributed graph processing, such as centrality, community detection, similarity, cohesive subgraph, traversal, pattern matching, and covering. The paper provides an overview of existing distributed graph processing frameworks and systems, including Pregel, Giraph, GraphX, and GraphScope. It also discusses the current research trends and potential future opportunities in this field. The paper concludes with a detailed analysis of the challenges and solutions in each algorithmic topic, providing insights into the potential directions for future research. The paper also presents a comprehensive graph that encapsulates the surveyed material, mapping out the intricate connections among papers, topics, algorithms, solutions, and challenges. This graph provides a visual narrative of the landscape of distributed graph algorithms on massive graphs.