Name
Abstract | ||
|---|---|---|
Identification of influential users in online social networks allows to facilitate efficient information diffusion to a large part of the network and thus benefiting diverse applications including viral marketing, disease control, and news dissemination. Existing methods have mainly relied on the network structure only for the detection of influential users. In this paper, we enrich this approach ... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TCSS.2019.2907553 | IEEE Transactions on Computational Social Systems |
Keywords | Field | DocType |
Sociology,Statistics,Twitter,Real-time systems,Diffusion processes,Knowledge engineering | Data mining,Population,Viral marketing,Social network,Disease control,Computer science,Artificial intelligence,Knowledge engineering,Missing data,Machine learning,Scalability,Network structure | Journal |
Volume | Issue | ISSN |
6 | 3 | 2329-924X |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ayan Kumar Bhowmick | 1 | 7 | 3.15 |
| Martin Gueuning | 2 | 2 | 1.71 |
| Jean-Charles Delvenne | 3 | 299 | 32.41 |
| Renaud Lambiotte | 4 | 920 | 64.98 |
| Bivas Mitra | 5 | 98 | 25.41 |