Preface I Core Material 1.Graph Theory2.Random Graph Models3.Centrality Measures4.Degree Correlations5.Community Detection6.Graph Embeddings7.HypergraphsII Complementary Material8.Detecting Overlapping Communities9.Embedding Graphs10.Network Robustness11.Road Networks

This book concentrates on mining networks, a subfield within data science. Data science uses scientific and computational tools to extract valuable knowledge from large data sets. Once data is processed and cleaned, it is analyzed and presented to support decision making processes. Data science and machine learning tools have become widely used in companies of all sizes. 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. Here are a few selected typical applications of mining networks: Community detection (which users on some social media platform 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 of the experiments presented in the book yet also include additional material.

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