Courses on Computational Social Science and Information Analysis
This page organizes course materials, reading notes, technical tutorials, and methodological documents. The current teaching modules focus on social network analysis, text mining, bibliometrics, and data-driven research methods for information resource management.
Course-oriented
Each course has its own syllabus, topic structure, learning objectives, and document-style sections.
Markdown-first
Each section is written as a Markdown document, making it easy to update, extend, and reuse.
Research-linked
The materials connect methods with real research tasks, including network construction and textual evidence analysis.
社会网络分析
Social Network Analysis
This course introduces the conceptual foundations, data structures, core indicators, visualization methods, and empirical applications of social network analysis. It is designed for students who want to analyze collaboration networks, citation networks, knowledge networks, and organizational relations.
文本挖掘
Text Mining and Computational Text Analysis
This course introduces text preprocessing, keyword extraction, topic modeling, text classification, sentiment analysis, and LLM-assisted evidence extraction. It is designed for research tasks involving academic literature, policy documents, patents, online communities, and business texts.
LLM、RAG 与知识图谱
Large Language Models, Retrieval-Augmented Generation, and Knowledge Graphs
This course introduces the foundations of large language models, retrieval-augmented generation, knowledge graph construction, GraphRAG, evaluation, and research applications. It is designed for students who want to build knowledge-grounded AI systems for literature analysis, policy intelligence, patent mining, healthcare information, and enterprise knowledge services.