Mining Complex Networks, 2E by Bogumił Kamiński (.PDF)
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Mining Complex Networks, 2nd Edition by Bogumił Kamiński, Paweł Prałat, François Théberge
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Overview: This book concentrates on mining networks, a subfield within Data Science. Many Data Science problems can be viewed as a study of some properties of complex networks in which nodes represent the entities that are being investigated, and edges represent relations between these entities. In these networks (for example, the Instagram and Facebook online social networks), nodes not only contain some useful information (such as the user’s profile, photos, and tags) but are also internally connected to other nodes (relations based on follower requests, similar users’ behaviour, age, and geographic location). Such networks are often large-scale, decentralized, and evolve dynamically over time. Mining complex networks to understand the principles governing the organization and the behaviour of such networks is crucial for a broad range of fields of study, including information and social sciences, economics, biology, and neuroscience. Most existing related books concentrate on theory. On the other hand, in our book the theoretical foundations are combined with practical experiments where students are expected to code and analyze graph datasets by themselves. This book is accompanied by Jupyter notebooks (in Python and Julia) which not only contain all of the experiments presented in the book but which also include additional material (In particular, Jupyter notebooks can be found here: github.com/ftheberge/GraphMiningNotebooks). In order to solve the two-language problem, in this book we provide implementations of the examples not only using the Python language but also using the Julia language. Julia, like Python, is a high-level language (actually, in many cases the code is quite similar), but at the same time it is compiled (as opposed to Python which is interpreted), which allows the execution speed of the programs to be comparable to languages such as C++. The book was written based on the lecture notes for a graduate course entitled Graph Mining which is offered to students enrolled in the Data Science and Analytics Master’s program at Toronto Metropolitan University (Toronto, Canada). This book is aimed to be suitable for an upper-year undergraduate course or a graduate course. Students in programs such as Data Science, mathematics, Computer Science, business, engineering, physics, statistics, and social science will benefit from courses that are based on this book.
Genre: Non-Fiction > Tech & Devices
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