Introduction to Algorithms, Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein, 2022 (The MIT Press) - A comprehensive and authoritative textbook covering fundamental data structures, graph representations, and algorithms like BFS, DFS, Dijkstra's, and Minimum Spanning Trees.
NetworkX Documentation, Aric A. Hagberg, Daniel A. Schult, and Pieter J. Swart, 2025 - The official documentation for NetworkX, an essential Python library for creating, manipulating, and studying the structure and function of complex networks.
Graph Neural Networks: A Review of Methods and Applications, Jie Zhou, Ganqu Cui, Zhengyu Chen, Ming Ding, Stefan Neumann, Jiaying Li, and Jie Tang, 2020IEEE Transactions on Neural Networks and Learning Systems, Vol. 32 (IEEE)DOI: 10.1109/TNNLS.2020.2974937 - A widely cited survey providing a comprehensive overview of Graph Neural Networks, their methodologies, and diverse applications in machine learning.
Data Structures and Algorithms in Python, Michael T. Goodrich, Roberto Tamassia, and Michael H. Goldwasser, 2013 (John Wiley & Sons) - A standard textbook that presents data structures and algorithms, including comprehensive coverage of graphs, with examples and implementations specifically in Python.