Name
Abstract | ||
|---|---|---|
Influence maximization problems aim to identify key players in (social) networks and are typically motivated from viral marketing. In this work, we introduce and study the Generalized Least Cost Influence Problem (GLCIP) that generalizes many previously considered problem variants and allows to overcome some of their limitations. A formulation that is based on the concept of activation functions is proposed together with strengthening inequalities. Exact and heuristic solution methods are developed and compared for the new problem. Our computational results also show that our approaches outperform the state-of-the-art on relevant, special cases of the GLCIP. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s10107-018-1288-y | Math. Program. |
Keywords | Field | DocType |
Influence maximization,Mixed-integer programming,Social network analysis,90B10,90C11,90C27 | Mathematical optimization,Viral marketing,Heuristic,Influence propagation,Social network,Social network analysis,Integer programming,Maximization,Mathematics | Journal |
Volume | Issue | ISSN |
170 | 1 | 0025-5610 |
Citations | PageRank | References |
0 | 0.34 | 13 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matteo Fischetti | 1 | 2505 | 260.53 |
| Michael Kahr | 2 | 0 | 0.34 |
| Markus Leitner | 3 | 148 | 16.62 |
| Michele Monaci | 4 | 1049 | 60.78 |
| Mario Ruthmair | 5 | 71 | 8.40 |