Computational approaches to the network science of teams:
Business operations in large organizations today involve massive, interactive, and layered networks of teams and personnel collaborating across hierarchies and countries on complex tasks. To optimize productivity, businesses need to know: what communication patterns do high-performing teams have in...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Weitere beteiligte Personen: | |
| Format: | E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2021
|
| Links: | https://doi.org/10.1017/9781108683173 |
| Zusammenfassung: | Business operations in large organizations today involve massive, interactive, and layered networks of teams and personnel collaborating across hierarchies and countries on complex tasks. To optimize productivity, businesses need to know: what communication patterns do high-performing teams have in common? Is it possible to predict a team's performance before it starts work on a project? How can productive team behavior be fostered? This comprehensive review for researchers and practitioners in data mining and social networks surveys recent progress in the emerging field of network science of teams. Focusing on the underlying social network structure, the authors present models and algorithms characterizing, predicting, optimizing, and explaining team performance, along with key applications, open challenges, and future trends. |
| Umfang: | 1 Online-Ressource (viii, 158 Seiten) |
| ISBN: | 9781108683173 |
Internformat
MARC
| LEADER | 00000nam a2200000 i 4500 | ||
|---|---|---|---|
| 001 | ZDB-20-CTM-CR9781108683173 | ||
| 003 | UkCbUP | ||
| 005 | 20201123134455.0 | ||
| 006 | m|||||o||d|||||||| | ||
| 007 | cr|||||||||||| | ||
| 008 | 181023s2021||||enk o ||1 0|eng|d | ||
| 020 | |a 9781108683173 | ||
| 100 | 1 | |a Li, Liangyue |d 1989- | |
| 245 | 1 | 0 | |a Computational approaches to the network science of teams |c Liangyue Li, Hanghang Tong |
| 264 | 1 | |a Cambridge |b Cambridge University Press |c 2021 | |
| 300 | |a 1 Online-Ressource (viii, 158 Seiten) | ||
| 336 | |b txt | ||
| 337 | |b c | ||
| 338 | |b cr | ||
| 520 | |a Business operations in large organizations today involve massive, interactive, and layered networks of teams and personnel collaborating across hierarchies and countries on complex tasks. To optimize productivity, businesses need to know: what communication patterns do high-performing teams have in common? Is it possible to predict a team's performance before it starts work on a project? How can productive team behavior be fostered? This comprehensive review for researchers and practitioners in data mining and social networks surveys recent progress in the emerging field of network science of teams. Focusing on the underlying social network structure, the authors present models and algorithms characterizing, predicting, optimizing, and explaining team performance, along with key applications, open challenges, and future trends. | ||
| 700 | 1 | |a Tong, Hanghang | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 9781108498548 |
| 966 | 4 | 0 | |l DE-91 |p ZDB-20-CTM |q TUM_PDA_CTM |u https://doi.org/10.1017/9781108683173 |3 Volltext |
| 912 | |a ZDB-20-CTM | ||
| 912 | |a ZDB-20-CTM | ||
| 049 | |a DE-91 | ||
Datensatz im Suchindex
| DE-BY-TUM_katkey | ZDB-20-CTM-CR9781108683173 |
|---|---|
| _version_ | 1825574046881808384 |
| adam_text | |
| any_adam_object | |
| author | Li, Liangyue 1989- |
| author2 | Tong, Hanghang |
| author2_role | |
| author2_variant | h t ht |
| author_facet | Li, Liangyue 1989- Tong, Hanghang |
| author_role | |
| author_sort | Li, Liangyue 1989- |
| author_variant | l l ll |
| building | Verbundindex |
| bvnumber | localTUM |
| collection | ZDB-20-CTM |
| format | eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01584nam a2200253 i 4500</leader><controlfield tag="001">ZDB-20-CTM-CR9781108683173</controlfield><controlfield tag="003">UkCbUP</controlfield><controlfield tag="005">20201123134455.0</controlfield><controlfield tag="006">m|||||o||d||||||||</controlfield><controlfield tag="007">cr||||||||||||</controlfield><controlfield tag="008">181023s2021||||enk o ||1 0|eng|d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781108683173</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Li, Liangyue</subfield><subfield code="d">1989-</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Computational approaches to the network science of teams</subfield><subfield code="c">Liangyue Li, Hanghang Tong</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cambridge</subfield><subfield code="b">Cambridge University Press</subfield><subfield code="c">2021</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (viii, 158 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Business operations in large organizations today involve massive, interactive, and layered networks of teams and personnel collaborating across hierarchies and countries on complex tasks. To optimize productivity, businesses need to know: what communication patterns do high-performing teams have in common? Is it possible to predict a team's performance before it starts work on a project? How can productive team behavior be fostered? This comprehensive review for researchers and practitioners in data mining and social networks surveys recent progress in the emerging field of network science of teams. Focusing on the underlying social network structure, the authors present models and algorithms characterizing, predicting, optimizing, and explaining team performance, along with key applications, open challenges, and future trends.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tong, Hanghang</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">9781108498548</subfield></datafield><datafield tag="966" ind1="4" ind2="0"><subfield code="l">DE-91</subfield><subfield code="p">ZDB-20-CTM</subfield><subfield code="q">TUM_PDA_CTM</subfield><subfield code="u">https://doi.org/10.1017/9781108683173</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-20-CTM</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-20-CTM</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91</subfield></datafield></record></collection> |
| id | ZDB-20-CTM-CR9781108683173 |
| illustrated | Not Illustrated |
| indexdate | 2025-03-03T11:58:01Z |
| institution | BVB |
| isbn | 9781108683173 |
| language | English |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (viii, 158 Seiten) |
| psigel | ZDB-20-CTM TUM_PDA_CTM ZDB-20-CTM |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Cambridge University Press |
| record_format | marc |
| spelling | Li, Liangyue 1989- Computational approaches to the network science of teams Liangyue Li, Hanghang Tong Cambridge Cambridge University Press 2021 1 Online-Ressource (viii, 158 Seiten) txt c cr Business operations in large organizations today involve massive, interactive, and layered networks of teams and personnel collaborating across hierarchies and countries on complex tasks. To optimize productivity, businesses need to know: what communication patterns do high-performing teams have in common? Is it possible to predict a team's performance before it starts work on a project? How can productive team behavior be fostered? This comprehensive review for researchers and practitioners in data mining and social networks surveys recent progress in the emerging field of network science of teams. Focusing on the underlying social network structure, the authors present models and algorithms characterizing, predicting, optimizing, and explaining team performance, along with key applications, open challenges, and future trends. Tong, Hanghang Erscheint auch als Druck-Ausgabe 9781108498548 |
| spellingShingle | Li, Liangyue 1989- Computational approaches to the network science of teams |
| title | Computational approaches to the network science of teams |
| title_auth | Computational approaches to the network science of teams |
| title_exact_search | Computational approaches to the network science of teams |
| title_full | Computational approaches to the network science of teams Liangyue Li, Hanghang Tong |
| title_fullStr | Computational approaches to the network science of teams Liangyue Li, Hanghang Tong |
| title_full_unstemmed | Computational approaches to the network science of teams Liangyue Li, Hanghang Tong |
| title_short | Computational approaches to the network science of teams |
| title_sort | computational approaches to the network science of teams |
| work_keys_str_mv | AT liliangyue computationalapproachestothenetworkscienceofteams AT tonghanghang computationalapproachestothenetworkscienceofteams |