Paper Info
Title
Forming a research team of experts in expert-skill co-occurrence network of research news.
Abstract
The team formation problem is required to find a group of individuals that can match the skills required by a collaborative task. Large-scale and comprehensive scientific research tasks need skilled experts from various fields to form a research team and work for it. This paper constructs a dataset and proposes team formation algorithms to find out research teams, which provides decision support for the research projects. The size of existing datasets is relatively small and fields of experts in it are less diversified. This paper extracts information of experts and skills from research news to construct a co-occurrence network with heterogeneous network structure. Based on the dataset, this work designs approximate algorithms regarding skill as the priority to find near optimum teams with provable guarantees. On heterogeneous structure, the proposed algorithms directly search requested skills to form the subgraph of team, which achieve significant improvement in time efficiency. Experimental results suggest that our methods can form the high-quality research team, and have better efficiently compared to naive strategies and scale well with the size of the data.
Year
DOI
Venue
2016
10.5555/3192424.3192637
ASONAM '16: Advances in Social Networks Analysis and Mining 2016 Davis California August, 2016
Keywords
Field
DocType
team formation, decision support, research news, heterogeneous network, approximation algorithms
Data mining,Approximation algorithm,Algorithm design,Computer science,Decision support system,Co-occurrence,Artificial intelligence,Heterogeneous network,Machine learning,Scientific method
Conference
ISBN
Citations 
PageRank 
978-1-5090-2846-7
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Juan Yang162.78
Mengxin Li200.68
Bin Wu329052.43
Chenyang Xu458523.07