TY - GEN

T1 - Optimal budget allocation

T2 - 31st International Conference on Machine Learning, ICML 2014

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.).

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M3 - Conference contribution

AN - SCOPUS:84919785397

T3 - 31st International Conference on Machine Learning, ICML 2014

SP - 556

EP - 568

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

PB - International Machine Learning Society (IMLS)

Y2 - 21 June 2014 through 26 June 2014

ER -