Statistical and Machine Learning Approaches for Network Analysis (eBook)

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  • 96,927 Words
  • 344 Pages

Explore the multidisciplinary nature of complex networksthrough machine learning techniques

Statistical and Machine Learning Approaches for NetworkAnalysis provides an accessible framework for structurallyanalyzing graphs by bringing together known and novel approaches ongraph classes and graph measures for classification. By providingdifferent approaches based on experimental data, the book uniquelysets itself apart from the current literature by exploring theapplication of machine learning techniques to various types ofcomplex networks.

Comprised of chapters written by internationally renownedresearchers in the field of interdisciplinary network theory, thebook presents current and classical methods to analyze networksstatistically. Methods from machine learning, data mining, andinformation theory are strongly emphasized throughout. Real datasets are used to showcase the discussed methods and topics, whichinclude:

  • A survey of computational approaches to reconstruct andpartition biological networks
  • An introduction to complex networks—measures, statisticalproperties, 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 NetworkAnalysis is an excellent supplemental text for graduate-level,cross-disciplinary courses in applied discrete mathematics,bioinformatics, pattern recognition, and computer science. The bookis also a valuable reference for researchers and practitioners inthe fields of applied discrete mathematics, machine learning, datamining, and biostatistics.

Explore the multidisciplinary nature of complex networksthrough machine learning techniques

Statistical and Machine Learning Approaches for NetworkAnalysis provides an accessible framework for structurallyanalyzing graphs by bringing together known and novel approaches ongraph classes and graph measures for classification. By providingdifferent approaches based on experimental data, the book uniquelysets itself apart from the current literature by exploring theapplication of machine learning techniques to various types ofcomplex networks.

Comprised of chapters written by internationally renownedresearchers in the field of interdisciplinary network theory, thebook presents current and classical methods to analyze networksstatistically. Methods from machine learning, data mining, andinformation theory are strongly emphasized throughout. Real datasets are used to showcase the discussed methods and topics, whichinclude:

  • A survey of computational approaches to reconstruct andpartition biological networks
  • An introduction to complex networks—measures, statisticalproperties, 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 NetworkAnalysis is an excellent supplemental text for graduate-level,cross-disciplinary courses in applied discrete mathematics,bioinformatics, pattern recognition, and computer science. The bookis also a valuable reference for researchers and practitioners inthe fields of applied discrete mathematics, machine learning, datamining, and biostatistics.


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