This book concentrates on mining networks, a subfield within data science. 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.
This book concentrates on mining networks, a subfield within data science. 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.
Bogumi¿ Kami¿ski 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. Bogumi¿ is an expert in applications of mathematical modelling 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. Pawe¿ Präat is a Professor of Mathematics at Ryerson University, whose main research interests are in random graph theory, especially in modelling and mining complex networks. He is the Director of Fields-CQAM Lab on Computational Methods in Industrial Mathematics at The Fields Institute for Research in Mathematical Sciences and has pursued collaborations with various industry partners as well as the Government of Canada. He has written over 170 papers and 3 books with 130 plus collaborators. François Théberge holds a B.Sc. degree in applied mathematics from the University of Ottawa, a M.Sc. 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. His current interests include relational-data mining and deep learning.
Inhaltsangabe
Preface I Core Material 1.Graph Theory 2.Random Graph Models 3.Centrality Measures 4.Degree Correlations 5.Community Detection 6.Graph Embeddings 7.Hypergraphs II Complementary Material 8.Detecting Overlapping Communities 9.Embedding Graphs 10.Network Robustness 11.Road Networks
