### Abstract

2014 We consider the budget allocation problem over bipartite influence model proposed by Alon et al. (Alon et al., 2012). This problem can be viewed as the well-known influence maximization problem with budget constraints. We first show that this problem and its much more general form fall into a general setting; namely the monotone submodular function maximization over integer lattice subject to a knapsack constraint. Our framework includes Alon et al.'s model, even with a competitor and with cost. We then give a (1 - 1/e)-approximation algorithm for this more general problem. Furthermore, when influence probabilities are nonincreasing, we obtain a faster (1 - 1/e)-approximation algorithm, which runs essentially in linear time in the number of nodes. This allows us to implement our algorithm up to almost 10M edges (indeed, our experiments tell us that we can implement our algorithm up to 1 billion edges. It would approximately take us only 500 seconds.).

Original language | English |
---|---|

Title of host publication | 31st International Conference on Machine Learning, ICML 2014 |

Publisher | International Machine Learning Society (IMLS) |

Pages | 556-568 |

Number of pages | 13 |

ISBN (Electronic) | 9781634393973 |

Publication status | Published - 2014 |

Externally published | Yes |

Event | 31st International Conference on Machine Learning, ICML 2014 - Beijing, China Duration: 2014 Jun 21 → 2014 Jun 26 |

### Publication series

Name | 31st International Conference on Machine Learning, ICML 2014 |
---|---|

Volume | 1 |

### Other

Other | 31st International Conference on Machine Learning, ICML 2014 |
---|---|

Country | China |

City | Beijing |

Period | 14/6/21 → 14/6/26 |

### ASJC Scopus subject areas

- Artificial Intelligence
- Computer Networks and Communications
- Software

## Fingerprint Dive into the research topics of 'Optimal budget allocation: Theoretical guarantee and efficient algorithm'. Together they form a unique fingerprint.

## Cite this

*31st International Conference on Machine Learning, ICML 2014*(pp. 556-568). (31st International Conference on Machine Learning, ICML 2014; Vol. 1). International Machine Learning Society (IMLS).