### 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 |
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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 |

Volume | 1 |

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 |

### Other

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

Country | China |

City | Beijing |

Period | 14/6/21 → 14/6/26 |

### Fingerprint

### ASJC Scopus subject areas

- Artificial Intelligence
- Computer Networks and Communications
- Software

### Cite this

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

**Optimal budget allocation : Theoretical guarantee and efficient algorithm.** / Soma, Tasuku; Kakimura, Naonori; Inaba, Kazuhiro; Kawarabayashi, Ken Ichi.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*31st International Conference on Machine Learning, ICML 2014.*vol. 1, International Machine Learning Society (IMLS), pp. 556-568, 31st International Conference on Machine Learning, ICML 2014, Beijing, China, 14/6/21.

}

TY - GEN

T1 - Optimal budget allocation

T2 - Theoretical guarantee and efficient algorithm

AU - Soma, Tasuku

AU - Kakimura, Naonori

AU - Inaba, Kazuhiro

AU - Kawarabayashi, Ken Ichi

PY - 2014

Y1 - 2014

N2 - 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.).

AB - 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.).

UR - http://www.scopus.com/inward/record.url?scp=84919785397&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84919785397&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84919785397

VL - 1

SP - 556

EP - 568

BT - 31st International Conference on Machine Learning, ICML 2014

PB - International Machine Learning Society (IMLS)

ER -