Statistical and Machine Learning Approaches for Network Analysis / Wiley Series in Computational Statistics (PDF)

Explore the multidisciplinary nature of complex networks
through machine learning techniques

Statistical and Machine Learning Approaches for Network
Analysis provides an accessible framework for structurally
analyzing graphs by bringing together known and novel approaches on
graph classes and graph measures for classification. By providing
different approaches based on experimental data, the book uniquely
sets itself apart from the current literature by exploring the
application of machine learning techniques to various types of
complex networks.

Comprised of chapters written by internationally renowned
researchers in the field of interdisciplinary network theory, the
book presents current and classical methods to analyze networks
statistically. Methods from machine learning, data mining, and
information theory are strongly emphasized throughout. Real data
sets are used to showcase the discussed methods and topics, which
include:

* A survey of computational approaches to reconstruct and
partition biological networks

* An introduction to complex networks--measures, statistical
properties, and models

* Modeling for evolving biological networks

* The structure of an evolving random bipartite graph

* Density-based enumeration in structured data

* Hyponym extraction employing a weighted graph kernel

Statistical and Machine Learning Approaches for Network
Analysis is an excellent supplemental text for graduate-level,
cross-disciplinary courses in applied discrete mathematics,
bioinformatics, pattern recognition, and computer science. The book
is also a valuable reference for researchers and practitioners in
the fields of applied discrete mathematics, machine learning, data
mining, and biostatistics.