This textbook for upper-year undergraduates and graduates concentrates on mining networks, a subfield within data science. Changes and updates to this edition include: new material and examples on random geometric graphs, new tools and techniques on mining hypergraphs, and a chapter on fairness in network mining models.
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, Instagram and Facebook online social networks), nodes not only contain some useful information (such as the user’s profile, photos, tags) but are also internally connected to other nodes (relations based on follower requests, similar users’ behavior, age, 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 behavior of such networks is crucial for a broad range of fields of study, including information and social sciences, economics, biology, and neuroscience.
The field has seen significant advancements since the first edition was published. Changes and updates to this edition include:
- New material and examples on random geometric graphs
- The chapter on node embeddings was augmented in several places including a discussion on classical vs. structural embeddings, more details on graph neural networks (GNNs), as well as other directions.
- Several new tools and techniques are introduced on mining hypergraphs
- New material on post-processing for overlapping communities
- A new focus on a framework for embedding graphs codeveloped by the authors.
- A short chapter on fairness in network mining models.
This book is aimed at being suitable for an upper-year undergraduate course or a graduate course.
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