Automatic role identification for research teams with ranking multi-view machines

Ni, Weijian; Guo, Haoyu; Liu, Tong; Zeng, Qingtian

doi:10.1007/s10115-020-01504-w

Cited by 1 publication

(1 citation statement)

References 45 publications

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“…There are many independent teams within a large research team, and the distribution and organizational structure of these smaller teams are similar, providing the basis for applications that make our approach work on real data. Some existing methods, such as those based on NMF(non-negative matrix factorization) [1] [2] are unsupervised methods based on role equivalence reasoning and are difficult to interpret [3] . Traditional random walk based algorithms such as Deepwalk [4] , Node2vec [5] , Struc2vec [6] , and CENALP [23] walk on one or two networks, analyze and predict some nodes but cannot walk on more than two networks.…”

Section: Introductionmentioning

confidence: 99%

Research on team role recognition based on multi-network representation learning

Wang¹

2023

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022)

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Team role research has been a hot research issue in recent years, and a good team background is very important for team role research. A good team is not a simple collection of multiple people but many people with different roles and functions (leadership, group leader, staff), they have close cooperation and strict relationship between superiors and subordinates. Team role research helps us to understand the function and behavior of the members of the team. Compared with the traditional walking method, Mjwalk can walk on multiple independent team networks, showing advantages in multinetwork joint learning. The conv_att method proposed in this paper solves the downstream role recognition task and improves the recognition effect. We evaluate the performance of the Mjwalk and conv_att framework with real team data, and improve f1_score and accuracy.

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Section: Introductionmentioning

confidence: 99%

Research on team role recognition based on multi-network representation learning

Wang¹

2023

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022)

View full text Add to dashboard Cite

show abstract

Automatic role identification for research teams with ranking multi-view machines

Cited by 1 publication

References 45 publications

Research on team role recognition based on multi-network representation learning

Research on team role recognition based on multi-network representation learning

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