Paper Info
Title
A generic visualization framework for understanding missing links in bipartite networks.
Abstract
The analysis of bipartite networks is critical in many application domains, such as studying gene expression in bio-informatics. One important task is missing link prediction, which infers the existence of new links based on currently observed ones. However, in practice, analysts need to utilize their domain knowledge based on the algorithm outputs in order to make sense of the results. We propose a novel visual analysis framework, MissBi, which allows for examining and understanding missing links in bipartite networks. Some initial feedback from a management school professor has demonstrated the effectiveness of the tool.
Year
DOI
Venue
2018
10.1145/3283289.3283338
SA '18: SIGGRAPH Asia 2018 Tokyo Japan December, 2018
Field
DocType
ISBN
Computer vision,Domain knowledge,Visualization,Computer science,Bipartite graph,Theoretical computer science,Artificial intelligence
Conference
978-1-4503-6063-0
Citations 
PageRank 
References 
1
0.35
4
Authors
3
Name
Order
Citations
PageRank
Jian Zhao140526.77
Francine Chen21218153.96
Patrick Chiu3144.57