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MASAHIRO KIMURA, Ph. D.

Professor
Faculty of Advanced Science and Technology
Ryukoku University

Email: kimura@rins.ryukoku.ac.jp
Tel: +81 77 543 7406
Fax: +81 77 543 7428
Address: Faculty of Advanced Science and Technology, Ryukoku University, Seta, Otsu 520-2194, Japan



Recent Papers

  • Masahiro Ikeda, Masahito Kumano, Joao Gama, and Masahiro Kimura: Simplicial Closure in Significant Higher-Order Network among Cooking Ingredients, Book of Abstract of the 9th International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2020), in press.

  • Teru Fujii, Masahito Kumano, Joao Gama, and Masahiro Kimura: Detecting Geographical Competitive Structure for POI Visit Dynamics, Proceedings of the 9th International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2020), in press.

  • Takayasu Fushimi, Kazumi Saito, Kouzou Ohara, Masahiro Kimura, and Hiroshi Motoda: Efficient Computing of PageRank Scores on Exact Expected Transition Matrix of Large Uncertain Graph, Proceedings of 2020 IEEE International Conference on Big Data (BigData 2020), in press.

  • Takayasu Fushimi, Kazumi Saito, Kouzou Ohara, Masahiro Kimura, and Hiroshi Motoda: Opening and Closing Dynamics of Competing Shop Groups over Spatial Networks, Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM 2020), in press.

  • Kouzou Ohara, Takayasu Fushimi, Kazumi Saito, Masahiro Kimura, and Hiroshi Motoda: Maximizing Network Coverage Under the Presence of Time Constraint by Injecting Most Effective k-Links, Proceedings of the 23rd International Conference on Discovery Science (DS 2020), LNAI 12323, pp.421-436, Springer, 2020.

  • Mario Cordeiro, Rui Portocarrero Sarmento, Pavel Brazdil, Masahiro Kimura, Joao Gama: Identifying, Ranking and Tracking Community Leaders in Evolving Social Networksk, Proceedings of the 8th International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2019), Studies in Computational Intelligence 881, pp.198-210, Springer, 2020.

  • Kouzou Ohara, Kazumi Saito, Masahiro Kimura, and Hiroshi Motoda: Resampling-based predictive simulation framework of stochastic diffusion model for identifying top-K influential nodes, International Journal of Data Science and Analytics, Vol.9, No.2, pp.175-195, 2020.

  • Minoru Higuchi, Kanji Matsutani, Masahito Kumano, Masahiro Kimura: Extracting influence structure in geographical attention dynamics, IEICE Transactions on Information and Systems (Japanese Edition), Vol.J102-D, No.10, pp.685-697, 2019.

  • Kazumi Saito, Kouzou Ohara, Masahiro Kimura, and Hiroshi Motoda: Resampling-Based Framework for Unbiased Estimator of Node Centrality over Large Complex Network, Proceedings of the 22nd International Conference on Discovery Science (DS 2019), LNAI 11828, pp.428-442, Springer, 2019.

  • Kouzou Ohara, Kazumi Saito, Masahiro Kimura, and Hiroshi Motoda: Critical node identification based on articulation point detection for uncertain network, International Journal of Networking and Computing, Vol.9, No.2, pp.201-216, 2019.

  • Kazumi Saito, Kouzou Ohara, Masahiro Kimura, and Hiroshi Motoda: Efficient Identification of Critical Links Based on Reachability Under the Presence of Time Constraint, Proceedings of the 16th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2019), LNAI 11671, pp.404-418, Springer, 2019.

  • Minoru Higuchi, Kanji Matsutani, Masahito Kumano, and Masahiro Kimura: Discovering spatio-temporal latent influence in geographical attention dynamics, In M. Berlingerio et al. (Eds.): Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2018), LNAI 11052, pp.517-534, Springer, 2019.

  • Keiko Ono, Yoshiko Hanada, Masahito Kumano, and Masahiro Kimura: Enhancing island model genetic programming by controlling frequent trees, Journal of Artificial Intelligence and Soft Computing Research, Vol.9, No.1, pp.51-65, 2019.

  • Kouzou Ohara, Kazumi Saito, Masahiro Kimura, and Hiroshi Motoda: Critical node identification based on articulation point detection for network with uncertain connectivity, Proceedings of the 6th International Symposium on Computing and Networking (CANDAR'18), pp.146-152, IEEE, 2018.

  • Kazumi Saito, Kouzou Ohara, Masahiro Kimura, and Hiroshi Motoda: Critical link identification based on bridge detection for network with uncertain connectivity, Proceedings of the 24th International Symposium on Methodologies for Intelligent Systems (ISMIS 2018), LNAI 11177, pp.88-99, Springer, 2018.

  • Kazumi Saito, Kouzou Ohara, Masahiro Kimura, and Hiroshi Motoda: Accurate and efficient detection of critical links in network to minimize information loss, Journal of Intelligent Information Systems, Vol.51, No.2, pp.235-255, 2018.

  • Kazumi Saito, Kouzou Ohara, Masahiro Kimura, and Hiroshi Motoda: Efficient detection of critical links to maintain performance of network with uncertain connectivity, Proceedings of the 15th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2018), LNAI 11012, pp.282-295, Springer, 2018.

  • Yuuki Kikuchi, Masahito Kumano, and Masahiro Kimura: Extracting active co-occurrence patterns of cooking ingredient usage in recipe sharing sites, IPSJ Transactions on Mathematical Modeling and its Applications, Vol.11, No.2, pp.30-40, 2018, in Japanese.

  • Kanji Matsutani, Masahito Kumano, Masahiro Kimura, Kazumi Saito, Kouzou Ohara, and Hiroshi Motoda: Extracting cooperative structure among online items from share-event sequence, Transactions of the Japanese Society for Artificial Intelligence, Vol.33, No.3, p.A-HA1_1-12, 2018, in Japanese.

  • Masahito Kumano, Kazuya Hashimoto, and Masahiro Kimura: Digest visualization of photo spots in terms of seasonal uniqueness, Journal of the Society for Art and Science, Vol.17, No.1, pp.8-25, 2018, in Japanese.

  • Kazumi Saito, Kouzou Ohara, Masahiro Kimura, and Hiroshi Motoda: Which is more influential, "who" or "when" for a user to rate in online review site?, Intelligent Data Analysis, Vol.22, No.3, pp.639-657, 2018.


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    Last modified on: November 2, 2020.