The steps above mirror the algorithmic treatment given in the textbook and can be extended to larger data sets. | Q | A | |------|-------| | Is there a free version of the book? | No complete free version is legally available. The author may share individual lecture slides or a pre‑print chapter on a personal or institutional website, but the full textbook remains under copyright. | | Can I share a PDF copy with classmates? | Sharing a full copyrighted PDF without permission violates copyright law. Instead, encourage classmates to obtain the book through the legitimate channels listed above. | | Is the book suitable for self‑study? | Yes. Each chapter contains clear explanations, examples, and exercises. Complementary online tutorials (e.g., NetworkX docs) can help reinforce the material. | | Does the book cover graph neural networks (GNNs)? | An introductory overview appears in the “Advanced Topics” section (Chapter 18). For an in‑depth treatment, see dedicated GNN textbooks or recent survey papers. | | What software is recommended for the exercises? | Python (NetworkX, NumPy, SciPy, scikit‑learn) is the primary environment, but MATLAB, R (igraph), or Julia are also viable. | 10. Conclusion “Network Theory” by Smarajit Ghosh is a well‑structured, mathematically rigorous, yet approachable text that equips readers with the tools to model, analyze, and engineer complex networks. By following the legitimate acquisition routes outlined above, you can gain full access to the material and start applying its concepts to real‑world problems.