A First Course in Network Science 1st Edition

I enjoyed reading this textbook for many reasons which are summarized below. I highly recommend it for teaching intro. to network science courses to sophomore/junior students (Chapters 0 -- 4), as well as senior/graduate students (Chapters 5 -- 7). I currently use it to teach Intro. to Network Science to undergraduates.* Clear and concise: Overall, the writing is clear and approachable. Mathematical formalism are unpacked and explained. The textbook is not long, but surprisingly packs a lot of information.* Real-world examples and real-world problems: From online social networks to transportation networks, throughout the textbook, concepts are explained with real-world examples, thus. making the abstract ideas relatable. Additionally, when discussion social networks, the writers explore important challenges (misinformation diffusion and echo chambers) plaguing online social networks.* Info-boxes: You'd learn more than network science in this textbook. I really appreciate how the authors included important ideas in info-boxes. For example, an info-box summarized why the logarithm scale is useful in visualization very small and very large quantities in the same plot.* Code examples: Documented-code examples are provided across all chapters. And the textbook provides a Github repo with documented Python notebooks.* Progression: The progression of the textbook is well-thought out: Chapter 0 (introduction) inspires you to continue reading by showing the broad applicability of network science. Chapter 1 (Network elements), takes you on a tour to visit important network concepts. Chapters 2 -- 4 (Small worlds, Hubs, and Direction & Weights) focus on important concepts that are prominent in many real-world networks. Chapters 5 -- 7 (Network models, communities, and dynamics) explores more advanced concepts. Overall the textbook strikes the right balance between fundamental/introductory materials and advanced concepts.

I love this book. Nice color visuals, smooth pages that smell good. The content? Oh, sorry about the tangent. I should mention that the author does a fantastic job of presenting this material and separates the technical (math and its notation) from the non-technical material so those not interested in the more complex details can continue reading. Presentation is clear and concise. The coverage of python's networkx package is clear yet thorough. Absolute beginners might struggle a bit, but those of any other level of python experience will be fine. Enjoy the online tutorials in the GitHub repo! The exercises in each chapter are easy to understand and tend to build in difficulty.Finish this book, and you'll have a solid understanding of network science and its older cousin graph theory. Demand a sequel!

This is a great book for anyone getting started with network science. There is a bit of light-weight theory in text boxes. The use of the Python NetworkX package gives you a way to try out some of the concepts for yourself.

It's fine. As somebody looking to brush up on network models for doing some data analysis on social networks I thought it was good but it was just a bit cursory. I would really like to see more code examples, problems, and applications in a new edition.

Amazing book, very well written, easy to follow!

Excellent balance of approachabilty and mathematical detail; suitable to newcomers to the topic. The authors also supply helpful code overviews and a useful repository of Jupyter notebooks to introduce Python libraries for graph analysis.

I had no prior knowledge of network science before reading this book. Although it is a book meant for beginners, I still felt that it was rather difficult to understand. That issue may lie within me though. The beginning of the textbook is a nice introduction to what network science is. I used the book as part of a course so I have a different perspective than others who just read the book. The Python NetworkX code I used heavily as part of assignments. If you are just reading the book I would still recommend trying the examples from the textbook as you read. The textbook is a solid introduction to network science.

More for computer science than social science and not much practical application