Name
Abstract | ||
|---|---|---|
Although influence maximization problem has been extensively studied over the past ten years, majority of existing work adopt one of the following models: emph{full-feedback model} or emph{zero-feedback model}. In the zero-feedback model, we have to commit the seed users all at once in advance, this strategy is also known as non-adaptive policy. In the full-feedback model, we select one seed at a time and wait until the diffusion completes, before selecting the next seed. Full-feedback model has better performance but potentially huge delay, zero-feedback model has zero delay but poorer performance since it does not utilize the observation that may be made during the seeding process. To fill the gap between these two models, we propose emph{Partial-feedback Model}, which allows us to select a seed at any intermediate stage. We develop a novel $alpha$-greedy policy that, for the first time, achieves a bounded approximation ratio. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.24963/ijcai.2017/546 | IJCAI |
Field | DocType | Citations |
Data mining,Mathematical optimization,Computer science,Commit,Operations research,Maximization,Bounded function | Conference | 2 |
PageRank | References | Authors |
0.38 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Yuan | 1 | 237 | 11.92 |
| Tang Shaojie | 2 | 2224 | 157.73 |