Name
Abstract | ||
|---|---|---|
The selection and prioritization of research directions are always challenges. This paper aims to make sense of nanomaterial toxicity publication and keywords data through quantitative metrics and network visualization. We have adapted a combined approach of network analysis, cooccurrence analysis, clustering analysis and visual analytics, to characterize important relational properties of network structures and features of entities. The results show that both co-authorship network and keywords network on nanomaterial toxicity follow the power-law degree distribution. In addition, the co-authorship network appears to be of scalefree pattern. We also investigate and visualize the research trends in field of nanomaterial toxicity by studying top influence researchers and keywords over years. These findings offer researchers various insights of the patterns and trends in the nanomaterial toxicity. |
Year | Venue | Keywords |
|---|---|---|
2013 | 2013 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) | network science, visual analytics, co-authorship network, nanomaterial toxicity |
Field | DocType | ISSN |
Graph drawing,Data science,Network science,Computer science,Visual analytics,Prioritization,Artificial intelligence,Degree distribution,Network analysis,Cluster analysis,Network on,Bioinformatics,Machine learning | Conference | 2156-1125 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hui Yang | 1 | 1 | 0.70 |
| Soundar Kumara | 2 | 536 | 43.18 |
| Kaizhi Tang | 3 | 32 | 4.94 |
| Xiong Liu | 4 | 27 | 3.69 |
| Zheng Chen | 5 | 0 | 0.34 |
| Roger Xu | 6 | 111 | 14.71 |