Name
Abstract | ||
|---|---|---|
Influence maximization (IM) aims at maximizing the spread of influence by offering discounts to influential users (called seeding). In many applications, due to user’s privacy concern, overwhelming network scale etc., it is hard to target any user in the network as one wishes. Instead, only a small subset of users is initially accessible. Such access limitation would significantly impair the influ... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TKDE.2020.3015387 | IEEE Transactions on Knowledge and Data Engineering |
Keywords | DocType | Volume |
Social network services,Resource management,Adaptation models,Uncertainty,Approximation algorithms,Probabilistic logic | Journal | 34 |
Issue | ISSN | Citations |
6 | 1041-4347 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feng Chen | 1 | 218 | 46.85 |
| Luoyi Fu | 2 | 415 | 58.53 |
| Bo Jiang | 3 | 18 | 11.25 |
| Haisong Zhang | 4 | 15 | 8.00 |
| Xinbing Wang | 5 | 2642 | 214.43 |
| Feilong Tang | 6 | 432 | 61.65 |
| guihai chen | 7 | 3537 | 317.28 |