This paper provides a comprehensive survey of heterogeneous information network (HIN) analysis, a field that has gained significant attention in recent years. HINs are characterized by their multi-typed objects and links, which distinguish them from homogeneous networks. The paper introduces fundamental concepts, such as object and link types, network schemas, and meta paths, and discusses their applications in various data mining tasks. It highlights the rich semantic information contained in HINs, which offers new opportunities and challenges for data mining. The paper reviews advancements in similarity measure, clustering, classification, link prediction, and ranking tasks on HINs, emphasizing the importance of considering meta paths and integrating additional information. The survey also explores the integration of HIN analysis with other data mining tasks, such as ranking and outlier detection, and discusses future research directions.This paper provides a comprehensive survey of heterogeneous information network (HIN) analysis, a field that has gained significant attention in recent years. HINs are characterized by their multi-typed objects and links, which distinguish them from homogeneous networks. The paper introduces fundamental concepts, such as object and link types, network schemas, and meta paths, and discusses their applications in various data mining tasks. It highlights the rich semantic information contained in HINs, which offers new opportunities and challenges for data mining. The paper reviews advancements in similarity measure, clustering, classification, link prediction, and ranking tasks on HINs, emphasizing the importance of considering meta paths and integrating additional information. The survey also explores the integration of HIN analysis with other data mining tasks, such as ranking and outlier detection, and discusses future research directions.