Beginner-Friendly GuidesIntroductory books that explain graph machine learning concepts in simple terms, ideal for readers new to the field.
Academic TextbooksComprehensive textbooks used in academic courses, covering theory, algorithms, and mathematical foundations.
Practical Case StudiesBooks that focus on real-world applications of graph machine learning, including industry and research examples.
Python-Focused ResourcesGuides that teach graph machine learning using Python libraries and frameworks.
Network Analysis SpecializationBooks that emphasize graph ML techniques for social networks, biological networks, and other connected data.
Hands-On Project BooksResources that include step-by-step projects to build and deploy graph ML models.
Advanced Research ReferencesIn-depth books covering cutting-edge research, algorithms, and theoretical advancements in graph ML.
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