Name
Abstract | ||
|---|---|---|
The influence maximization problem is widely studied, but previous studies have assumed that the cost to activate each seed node was identical. In this paper, we consider different activation costs and investigate a new problem: the budget-aware influence maximization problem (BIM). This problem is NP-hard, which motivates our interest in its approximation. We develop two greedy algorithms for BIM, namely, BG and GMUI, and show that GMUI obtains a solution that is provably . Then, we consider the average profit to activate a common user and introduce GMUN, an improved GMUI algorithm. Finally, we evaluate our algorithms with experiments using two large real social networks. The results show that GMUI performs best in terms of influence, whereas GMUN creates leverage between the budget and net income, indicating that the marginal net income/costs ratio for each seed node selected by GMUN can satisfy enterprises. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1080/01605682.2018.1489343 | JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY |
Keywords | Field | DocType |
Online social networks,influence maximization,social marketing,budget | Social network,Computer science,Knowledge management,Social marketing,Maximization,Management science | Journal |
Volume | Issue | ISSN |
70.0 | 8.0 | 0160-5682 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yezheng Liu | 1 | 145 | 24.69 |
| Lingfei Li | 2 | 0 | 0.34 |