Mining Complex Networks

2. Auflage 2026
ISBN: 978-1-041-26192-6
Verlag: Taylor & Francis

Buch, Englisch, 336 Seiten, Format (B × H): 156 mm x 234 mm

Reihe: Advances in Applied Mathematics

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.

Zielgruppe

Undergraduate Advanced

Autoren/Hrsg.

Kaminski, Bogumil

Pralat, Pawel

Théberge, c

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Mathematik | Informatik Mathematik Algebra Zahlentheorie
• Mathematik | Informatik Mathematik Mathematik Allgemein Diskrete Mathematik, Kombinatorik
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftsstatistik, Demographie
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

Part 1. Core Material  1. Graph Theory  2. Random Graph Models  3. Centrality Measures  4. Degree Correlations  5. Community Detection  6. Graph Embeddings  7. Hypergraphs  Part 2. Additional Material  8. Detecting Overlapping Communities  9. Embedding Graphs  10. Network Robustness  11. Road Networks  12. Fairness in Graph Mining

Über Autor(innen)

Bogumil Kaminski is the Chairman of the Scientific Council for the Discipline of Economics and Finance at SGH Warsaw School of Economics. He is an expert in applications of mathematical modelling and artificial intelligence models to solve complex real-life problems. He is also a substantial opensource contributor to the development of the Julia language and its package ecosystem.

Pawel Pralat is a Professor of Mathematics at Toronto Metropolitan University, whose main research interests are in random graph theory, especially in modelling and mining complex networks. He has pursued collaborations with various industry partners as well as the Government of Canada. He has written more than 230 papers and 4 books with more than 170 collaborators.

François Théberge holds a BSc degree in applied mathematics from the University of Ottawa, an MSc in telecommunications from INRS, and a PhD in electrical engineering from McGill University. He has been employed by the Government of Canada since 1996 during which he was involved in the creation of the data science team as well as the research group now known as the Tutte Institute for Mathematics and Computing. He also holds an adjunct professorial position in the Department of Mathematics and Statistics at the University of Ottawa.