Name
Abstract | ||
|---|---|---|
The analysis of bipartite networks is critical in a variety of application domains, such as exploring entity co-occurrences in intelligence analysis and investigating gene expression in bio-informatics. One important task is missing link prediction, which infers the existence of unseen links based on currently observed ones. In this paper, we propose MissBiN that involves analysts in the loop for making sense of link prediction results. MissBiN combines a novel method for link prediction and an interactive visualization for examining and understanding the algorithm outputs. Further, we conducted quantitative experiments to assess the performance of the proposed link prediction algorithm and a case study to evaluate the overall effectiveness of MissBiN. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/VISUAL.2019.8933639 | VIS |
Keywords | Field | DocType |
Visualization,Prediction algorithms,Measurement,Task analysis,Network topology,Predictive models,Tools | Computer science,Bipartite graph,Visual analytics,Theoretical computer science,Interactive visualization,Artificial intelligence,Intelligence analysis,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-7281-4941-7 | 1 | 0.35 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Zhao | 1 | 405 | 26.77 |
| Maoyuan Sun | 2 | 73 | 7.95 |
| Francine Chen | 3 | 1218 | 153.96 |
| Patrick Chiu | 4 | 199 | 20.38 |