Provides an overview of the developments and advances in the field of
network clustering and blockmodeling over the last 10 years
This book offers an integrated treatment of network clustering and
blockmodeling, covering all of the newest approaches and methods that
have been developed over the last decade. Presented in a comprehensive
manner, it offers the foundations for understanding network structures
and processes, and features a wide variety of new techniques addressing
issues that occur during the partitioning of networks across multiple
disciplines such as community detection, blockmodeling of valued
networks, role assignment, and stochastic blockmodeling.
Written by a team of international experts in the field, Advances in
Network Clustering and Blockmodeling offers a plethora of diverse
perspectives covering topics such as: bibliometric analyses of the
network clustering literature; clustering approaches to networks; label
propagation for clustering; and treating missing network data before
partitioning. It also examines the partitioning of signed networks,
multimode networks, and linked networks. A chapter on structured
networks and coarsegrained descriptions is presented, along with another
on scientific coauthorship networks. The book finishes with a section
covering conclusions and directions for future work. In addition, the
editors provide numerous tables, figures, case studies, examples,
datasets, and more.
- Offers a clear and insightful look at the state of the art in network
clustering and blockmodeling
- Provides an excellent mix of mathematical rigor and practical
application in a comprehensive manner
- Presents a suite of new methods, procedures, algorithms for
partitioning networks, as well as new techniques for visualizing
matrix arrays
- Features numerous examples throughout, enabling readers to gain a
better understanding of research methods and to conduct their own
research effectively
- Written by leading contributors in the field of spatial networks
analysis
Advances in Network Clustering and Blockmodeling is an ideal book for
graduate and undergraduate students taking courses on network analysis
or working with networks using real data. It will also benefit
researchers and practitioners interested in network analysis.
