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A01=Hanghang Tong
A01=Liangyue Li
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Age Group_Uncategorized
Author_Hanghang Tong
Author_Liangyue Li
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Computational Approaches to the Network Science of Teams

★★★★★
★★★★★

English

By (author): Hanghang Tong Liangyue Li

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. See more
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A01=Hanghang TongA01=Liangyue LiAge Group_UncategorizedAuthor_Hanghang TongAuthor_Liangyue Liautomatic-updateCategory1=Non-FictionCategory=GPSCategory=JHBCategory=KJMV3Category=UBJCategory=UNCategory=UTCOP=United KingdomDelivery_Delivery within 10-20 working daysLanguage_EnglishPA=In stockPrice_€20 to €50PS=Activesoftlaunch
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Product Details
  • Weight: 350g
  • Dimensions: 156 x 234mm
  • Publication Date: 03 Dec 2020
  • Publisher: Cambridge University Press
  • Publication City/Country: United Kingdom
  • Language: English
  • ISBN13: 9781108498548

About Hanghang TongLiangyue Li

Liangyue Li is an applied scientist at Amazon. He received his PhD in computer science from Arizona State University. He has served as a program committee member in top data-mining and artificial intelligence venues (such as SIGKDD ICML AAAI and CIKM). He has given a tutorial at WSDM 2018 KDD 2018 and a keynote talk at CIKM 2016 Workshop on Big Network Analytics (BigNet 2016). Hanghang Tong is an associate professor at University of Illinois Urbana-Champaign since August 2019 Before that he was an associate professor at Arizona State University an assistant professor at City College City University of New York a research staff member at IBM T.J. Watson Research Center and a postdoctoral fellow at Carnegie Mellon University Pennsylvania. He received his M.Sc. and Ph.D. degrees both in machine learning from Carnegie Mellon University in 2008 and 2009. His research interest is in large-scale data mining for graphs and multimedia. He received several awards including NSF CAREER award (2017) ICDM 10-Year Highest Impact Paper Award (2015) four best paper awards (TUP'14 CIKM'12 SDM'08 ICDM'06) six `bests of conference' (ICDM'18 KDD'16 SDM'15 ICDM'15 SDM'11 and ICDM'10) one best demo honorable mention (SIGMOD'17) and one best demo candidate second place (CIKM'17). He has published over 100 referred articles. He is the editor-in-chief of SIGKDD Explorations (ACM) an action editor of Data Mining and Knowledge Discovery (Springer) and an associate editor of Neurocomputing Journal (Elsevier); He has served as a program committee member in multiple data-mining database and artificial intelligence venues (including SIGKDD SIGMOD AAAI WWW and CIKM).

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