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Books > Computing & IT > Applications of computing > Databases
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Mining Complex Networks (Hardcover)
Loot Price: R2,724
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Mining Complex Networks (Hardcover)
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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 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. Bogumil Kaminski 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. Bogumil 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. Pawel Pralat is a Professor of Mathematics in
Ryerson University, whose main research interests are in random
graph theory, especially in modeling and mining complex networks.
He is the Director of Fields-CQAM Lab on Computational Methods in
Industrial Mathematics in 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 three books with 130 plus
collaborators. Francois Theberge 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 where 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.
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