Fast and Accurate Influence Maximization on Large Networks with Pruned Monte-Carlo Simulations N Ohsaka, T Akiba, Y Yoshida, K Kawarabayashi
Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI …, 2014
219 2014 Dynamic Influence Analysis in Evolving Networks N Ohsaka, T Akiba, Y Yoshida, K Kawarabayashi
Proceedings of the VLDB Endowment 9 (12), 1077–1088, 2016
97 2016 Monotone k-Submodular Function Maximization with Size Constraints N Ohsaka, Y Yoshida
Proceedings of the 29th Annual Conference on Neural Information Processing …, 2015
84 2015 Efficient PageRank Tracking in Evolving Networks N Ohsaka, T Maehara, K Kawarabayashi
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge …, 2015
71 2015 Coarsening Massive Influence Networks for Scalable Diffusion Analysis N Ohsaka, T Sonobe, S Fujita, K Kawarabayashi
Proceedings of the 2017 ACM SIGMOD International Conference on Management of …, 2017
34 2017 On the Power of Tree-Depth for Fully Polynomial FPT Algorithms Y Iwata, T Ogasawara, N Ohsaka
Proceedings of the 35th International Symposium on Theoretical Aspects of …, 2018
31 2018 Maximizing Time-Decaying Influence in Social Networks N Ohsaka, Y Yamaguchi, N Kakimura, K Kawarabayashi
Proceedings of the 15th European Conference on Machine Learning and …, 2016
31 2016 Portfolio Optimization for Influence Spread N Ohsaka, Y Yoshida
Proceedings of the 26th International Conference on World Wide Web (WWW 2017 …, 2017
29 2017 NoSingles: A Space-Efficient Algorithm for Influence Maximization D Popova, N Ohsaka, K Kawarabayashi, A Thomo
Proceedings of the 30th International Conference on Scientific and …, 2018
16 2018 The Solution Distribution of Influence Maximization: A High-level Experimental Study on Three Algorithmic Approaches N Ohsaka
Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020
15 2020 Tracking Regret Bounds for Online Submodular Optimization T Matsuoka, S Ito, N Ohsaka
Proceedings of the 24th International Conference on Artificial Intelligence …, 2021
9 2021 Gap Preserving Reductions Between Reconfiguration Problems N Ohsaka
Proceedings of the 40th International Symposium on Theoretical Aspects of …, 2022
8 2022 Reconfiguration Problems on Submodular Functions N Ohsaka, T Matsuoka
Proceedings of the 15th ACM International Conference on Web Search and Data …, 2022
8 2022 Maximization of Monotone -Submodular Functions with Bounded Curvature and Non- -Submodular Functions T Matsuoka, N Ohsaka
Proceedings of the 13th Asian Conference on Machine Learning (ACML 2021 …, 2021
7 2021 On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes N Ohsaka, T Matsuoka
Proceedings of the 37th International Conference on Machine Learning (ICML …, 2020
7 2020 Gap Amplification for Reconfiguration Problems N Ohsaka
Proceedings of the 35th Annual ACM-SIAM Symposium on Discrete Algorithms …, 2024
5 2024 Approximation Algorithm for Submodular Maximization under Submodular Cover N Ohsaka, T Matsuoka
Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence …, 2021
5 2021 On Approximate Reconfigurability of Label Cover N Ohsaka
arXiv preprint arXiv:2304.08746, 2023
4 2023 Some Inapproximability Results of MAP Inference and Exponentiated Determinantal Point Processes N Ohsaka
Journal of Artificial Intelligence Research 73, 709–735, 2022
4 2022 Unconstrained MAP Inference, Exponentiated Determinantal Point Processes, and Exponential Inapproximability N Ohsaka
Proceedings of the 24th International Conference on Artificial Intelligence …, 2021
4 2021