Citation Networks
Citation networks are graph-based representations of scholarly or scientific publications where nodes represent documents (e.g., research papers, patents, or articles) and edges represent citation relationships between them. They are used to analyze the structure, influence, and evolution of academic fields, identify key papers or authors, and detect research trends. This concept is foundational in bibliometrics, scientometrics, and information science for quantifying impact and mapping knowledge domains.
Developers should learn about citation networks when working on academic search engines, recommendation systems for research papers, or tools for analyzing scientific impact (e.g., in platforms like Google Scholar or Scopus). It is essential for building algorithms to compute metrics like the h-index, PageRank for papers, or community detection in research fields, often applied in data science, machine learning, and natural language processing projects involving scholarly data.