Mining Complex Networks

Networks are often large-scale, decentralized, and evolve dynamically over time. Mining complex networks aim to understand the principles governing the organization and the behavior of such networks is crucial for a broad range of fields of study. Here are a few selected typical applications of mining networks:
Community detection (which users on some social media platforms are close friends).
Link prediction (who is likely to connect to whom on such platforms).
Node attribute prediction (what advertisement should be shown to a given user of a particular platform to match their interests).
Influential node detection (which social media users would be the best ambassadors of a specific product).
This textbook is suitable for an upper-year undergraduate course or a graduate course in programs such as data science, mathematics, computer science, business, engineering, physics, statistics, and social science. This book can be successfully used by all enthusiasts of data science at various levels of sophistication to expand their knowledge or consider changing their career path.
Jupiter notebooks (in Python and Julia) accompany the book and can be accessed on

https://www.ryerson.ca/mining-complex-networks/. These not only contain all the experiments presented in the book, but also include additional material.

Bogumił Kamiński is the Chairman of the Scientific Council for the Discipline of Economics and Finance at SGH Warsaw School of Economics. He is also an Adjunct Professor at the Data Science Laboratory at Ryerson University. Bogumił is an expert in applications of mathematical modeling to solving complex real-life problems. He is also a substantial open-source contributor to the development of the Julia language and its package ecosystem.

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