Statistical and machine learning approaches for network analysis / edited by Matthias Dehmer, Subhash C. Basak.
"This book explores novel graph classes and presents novel methods to classify networks. It particularly addresses the following problems: exploration of novel graph classes and their relationships among each other; existing and classical methods to analyze networks; novel graph similarity and...
Saved in:
: | |
---|---|
TeilnehmendeR: | |
Year of Publication: | 2012 |
Language: | English |
Online Access: | |
Physical Description: | xii, 331 p. :; ill. |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
500894394 |
---|---|
ctrlnum |
(MiAaPQ)500894394 (Au-PeEL)EBL894394 (CaPaEBR)ebr10575598 (CaONFJC)MIL371402 (OCoLC)779740472 |
collection |
bib_alma |
record_format |
marc |
spelling |
Statistical and machine learning approaches for network analysis [electronic resource] / edited by Matthias Dehmer, Subhash C. Basak. Hoboken, N.J. : Wiley, 2012. xii, 331 p. : ill. Includes bibliographical references and index. "This book explores novel graph classes and presents novel methods to classify networks. It particularly addresses the following problems: exploration of novel graph classes and their relationships among each other; existing and classical methods to analyze networks; novel graph similarity and graph classification techniques based on machine learning methods; and applications of graph classification and graph mining. Key topics are addressed in depth including the mathematical definition of novel graph classes, i.e. generalized trees and directed universal hierarchical graphs, and the application areas in which to apply graph classes to practical problems in computational biology, computer science, mathematics, mathematical psychology, etc"-- Provided by publisher. Electronic reproduction. Ann Arbor, MI : ProQuest, 2015. Available via World Wide Web. Access may be limited to ProQuest affiliated libraries. Research Statistical methods. Machine theory. Communication Network analysis Graphic methods. Information science Statistical methods. Electronic books. Dehmer, Matthias, 1968- Basak, Subhash C., 1945- ProQuest (Firm) https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=894394 Click to View |
language |
English |
format |
Electronic eBook |
author2 |
Dehmer, Matthias, 1968- Basak, Subhash C., 1945- ProQuest (Firm) |
author_facet |
Dehmer, Matthias, 1968- Basak, Subhash C., 1945- ProQuest (Firm) ProQuest (Firm) |
author2_variant |
m d md s c b sc scb |
author2_role |
TeilnehmendeR TeilnehmendeR TeilnehmendeR |
author_corporate |
ProQuest (Firm) |
author_sort |
Dehmer, Matthias, 1968- |
title |
Statistical and machine learning approaches for network analysis |
spellingShingle |
Statistical and machine learning approaches for network analysis |
title_full |
Statistical and machine learning approaches for network analysis [electronic resource] / edited by Matthias Dehmer, Subhash C. Basak. |
title_fullStr |
Statistical and machine learning approaches for network analysis [electronic resource] / edited by Matthias Dehmer, Subhash C. Basak. |
title_full_unstemmed |
Statistical and machine learning approaches for network analysis [electronic resource] / edited by Matthias Dehmer, Subhash C. Basak. |
title_auth |
Statistical and machine learning approaches for network analysis |
title_new |
Statistical and machine learning approaches for network analysis |
title_sort |
statistical and machine learning approaches for network analysis |
publisher |
Wiley, |
publishDate |
2012 |
physical |
xii, 331 p. : ill. |
isbn |
9781118347010 (electronic bk.) |
callnumber-first |
Q - Science |
callnumber-subject |
Q - General Science |
callnumber-label |
Q180 |
callnumber-sort |
Q 3180.55 S7 S73 42012 |
genre |
Electronic books. |
genre_facet |
Electronic books. |
url |
https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=894394 |
illustrated |
Illustrated |
dewey-hundreds |
500 - Science |
dewey-tens |
510 - Mathematics |
dewey-ones |
511 - General principles of mathematics |
dewey-full |
511/.5 |
dewey-sort |
3511 15 |
dewey-raw |
511/.5 |
dewey-search |
511/.5 |
oclc_num |
779740472 |
work_keys_str_mv |
AT dehmermatthias statisticalandmachinelearningapproachesfornetworkanalysis AT basaksubhashc statisticalandmachinelearningapproachesfornetworkanalysis AT proquestfirm statisticalandmachinelearningapproachesfornetworkanalysis |
status_str |
n |
ids_txt_mv |
(MiAaPQ)500894394 (Au-PeEL)EBL894394 (CaPaEBR)ebr10575598 (CaONFJC)MIL371402 (OCoLC)779740472 |
is_hierarchy_title |
Statistical and machine learning approaches for network analysis |
author2_original_writing_str_mv |
noLinkedField noLinkedField noLinkedField |
_version_ |
1792330729074458625 |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02306nam a2200409 a 4500</leader><controlfield tag="001">500894394</controlfield><controlfield tag="003">MiAaPQ</controlfield><controlfield tag="005">20200520144314.0</controlfield><controlfield tag="006">m o d | </controlfield><controlfield tag="007">cr cn|||||||||</controlfield><controlfield tag="008">120308s2012 njua sb 001 0 eng d</controlfield><datafield tag="010" ind1=" " ind2=" "><subfield code="z"> 2012010295</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="z">9780470195154 (hardback)</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781118347010 (electronic bk.)</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(MiAaPQ)500894394</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(Au-PeEL)EBL894394</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(CaPaEBR)ebr10575598</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(CaONFJC)MIL371402</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)779740472</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">MiAaPQ</subfield><subfield code="c">MiAaPQ</subfield><subfield code="d">MiAaPQ</subfield></datafield><datafield tag="050" ind1=" " ind2="4"><subfield code="a">Q180.55.S7</subfield><subfield code="b">S73 2012</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">511/.5</subfield><subfield code="2">23</subfield></datafield><datafield tag="245" ind1="0" ind2="0"><subfield code="a">Statistical and machine learning approaches for network analysis</subfield><subfield code="h">[electronic resource] /</subfield><subfield code="c">edited by Matthias Dehmer, Subhash C. Basak.</subfield></datafield><datafield tag="260" ind1=" " ind2=" "><subfield code="a">Hoboken, N.J. :</subfield><subfield code="b">Wiley,</subfield><subfield code="c">2012.</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">xii, 331 p. :</subfield><subfield code="b">ill.</subfield></datafield><datafield tag="504" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references and index.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">"This book explores novel graph classes and presents novel methods to classify networks. It particularly addresses the following problems: exploration of novel graph classes and their relationships among each other; existing and classical methods to analyze networks; novel graph similarity and graph classification techniques based on machine learning methods; and applications of graph classification and graph mining. Key topics are addressed in depth including the mathematical definition of novel graph classes, i.e. generalized trees and directed universal hierarchical graphs, and the application areas in which to apply graph classes to practical problems in computational biology, computer science, mathematics, mathematical psychology, etc"--</subfield><subfield code="c">Provided by publisher.</subfield></datafield><datafield tag="533" ind1=" " ind2=" "><subfield code="a">Electronic reproduction. Ann Arbor, MI : ProQuest, 2015. Available via World Wide Web. Access may be limited to ProQuest affiliated libraries.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Research</subfield><subfield code="x">Statistical methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Machine theory.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Communication</subfield><subfield code="x">Network analysis</subfield><subfield code="x">Graphic methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Information science</subfield><subfield code="x">Statistical methods.</subfield></datafield><datafield tag="655" ind1=" " ind2="4"><subfield code="a">Electronic books.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dehmer, Matthias,</subfield><subfield code="d">1968-</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Basak, Subhash C.,</subfield><subfield code="d">1945-</subfield></datafield><datafield tag="710" ind1="2" ind2=" "><subfield code="a">ProQuest (Firm)</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=894394</subfield><subfield code="z">Click to View</subfield></datafield></record></collection> |