木　村　昌　弘

Email : kimura@rins.ryukoku.ac.jp
Tel : 077-543-7406
Fax : 077-543-7428
住所 : 〒520-2194　大津市瀬田大江町横谷1-5　龍谷大学理工学部電子情報学科

プロフィール

研究テーマ

• 複雑現象の数理モデリング
• 神経計算
• 機械学習
• Webマイニング
• 複雑ネットワーク

学会活動

• Program Committee, Second International Workshop on Social Computing, Behavior Modeling, and Prediction (2009).
• Program Committee Member, 10th Pacific Rim International Conference on Artificial Intelligence (2008).
• Technical Committee Member, 2008 International Joint Conference on Neural Networks (2008).
• 情報処理学会 ネットワーク生態学研究グループ 運営委員 (2005年3月〜).
• 日本応用数理学会誌 編集委員 (2003年4月〜2006年4月).
• 電子情報通信学会論文誌 査読委員 (1999年6月〜).
• 立命館大学 大学院理工学研究科 情報システム学専攻 非常勤講師 (2004年9月26日〜2005年3月31日).
• 名城大学 理工学部 非常勤講師 (2004年6月12日).
• 日本数学会，人工知能学会，日本神経回路学会，日本応用数理学会，電子情報通信学会，各会員．

受賞歴

• 平成21年度日本応用数理学会論文賞, 2009.
• Best Paper Award (8th IEEE International Conference on Computer and Information Technology), 2008.
• 人工知能学会2004年度研究会優秀賞, 2005.

論文（査読あり）

ジャーナル論文:

• Masahiro Kimura, Kazumi Saito, Ryohei Nakano and Hiroshi Motoda: Extracting influential nodes on to a social network for information diffusion, Data Mining and Knowledge Discovery, Vol.20, No.1, 70-97, 2010.

• 木村昌弘，斉藤和巳，中野良平，元田浩: 社会ネットワークにおける有力ノード抽出のための情報拡散モデルの学習, 人工知能学会論文誌, Vol.25, No.1, 215-223, 2010.

• Masahiro Kimura, Kazumi Saito and Hiroshi Motoda: Blocking links to minimize contamination spread in a social network, ACM Transactions on Knowledge Discovery from Data, Vol.3, No.2, Article 9, 2009.

• 木村昌弘，斉藤和巳: PMM型主成分分析を用いた文書ストリームの主要潜在トピック抽出, 日本応用数理学会論文誌, Vol.18, No.3, 363-388, 2008.

• 福井健一，斉藤和巳，木村昌弘，沼尾正行: 自己組織化ネットワークによる動的クラスタの可視化編纂, 人工知能学会論文誌, Vol.23, No.5, 319-329, 2008.

• 木村昌弘，斉藤和巳，中野良平: 社会ネットワーク上の情報伝搬における強影響力ノード抽出の効率化, 電子情報通信学会論文誌, Vol.J91-D, No.4, 1004-1015, 2008.

• 福井健一，斉藤和巳，木村昌弘，沼尾正行: ベクトル表現可能な機械抽出トピックの定量的評価法, 情報処理学会論文誌:数理モデル化と応用, Vol.48, No.SIG6(TOM17), 1-11, 2007.

• 風間一洋，佐藤進也，斉藤和巳，木村昌弘: トラックバックネットワークに基づくSEOコミュニティの分析, 情報処理学会論文誌, Vol.47, No.3, 840-849, 2006.

• Masahiro Kimura, Kazumi Saito and Naonori Ueda: Modeling share dynamics by extracting competition structure, Physica D, Vol.198, Issues 1-2, 57-73, 2004.

• Masairo Kimura, Kazumi Saito and Naonori Ueda: Modeling of growing networks with directional attachment and communities, Neural Networks, Vol.17, No.7, 975-988, 2004.

• Masahiro Kimura, Kazumi Saito and Naonori Ueda: Modeling network growth with directional attachment and communities, Systems and Computers in Japan, Vol.35, No.8, 1-11, 2004.

• 木村昌弘，斉藤和巳，上田修功: 指向性アタッチメントとコミュニティーをもつ成長ネットワークモデル, 電子情報通信学会論文誌, Vol.J86-D-II, No.10, 1468-1479, 2003.

• Masahiro Kimura: On unique representations of certain dynamical systems produced by continuous-time recurrent neural networks, Neural Computation, Vol.14, No.12, 2981-2996, 2002.

• Masahiro Kimura and Ryohei Nakano: Dynamical systems produced by recurrent neural networks, Systems ans Computers in Japan, Vol.31, No.4, 77-86, 2000.

• 木村昌弘，中野良平: アフィン神経力学系の一意表現, 日本応用数理学会論文誌, Vol.9, No.2, 37-50, 1999.

• 木村昌弘，中野良平: リカレントニューラルネットワークが生成する力学系, 電子情報通信学会論文誌, Vol.J82-D-II, No.4, 818-828, 1999.

• Masahiro Kimura and Ryohei Nakano: Learning dynamical systems by recurrent neural networks from orbits, Neural Networks, Vol.11, No.9, 1589-1599, 1998.

• Masahiro Kimura, Takafumi Saito and Mikio Shinya: Surface deformation with differential geometric structures, Computer Aided Geomeric Design, Vol.13, No.3, 243-256, 1996.

• Masahiro Kimura: Homogeneous Einstein metrics on certain Kaehler C-spaces, Advanced Studies in Pure Mathematics, Vol.18-I, 303-320, 1990.

国際会議論文:

• Kazumi Saito, Masahiro Kimura, Kouzou Ohara, and Hiroshi Motoda: Behavioral analyses of information diffusion models by observed data of social network, In Proceedings of 2010 International Conference on Social Computing, Behavioral Modeling, and Prediction (SBP10), to appear.

• Kazumi Saito, Masahiro Kimura, Kouzou Ohara, and Hiroshi Motoda: Learning continuous-time information diffusion model for social behavioral data analysis, In Z. Zhou and T. Washio (Eds.), Advances in Machine Learning (ACML 2009), Lecture Notes in Artificial Intelligence, Vol. 5828, Springer (2009), 322-337.

• Kazumi Saito, Masahiro Kimura, and Hiroshi Motoda: Discovering influential nodes for SIS models in social networks, In J. Gama, V.S. Costa, A.M. Jorge, and P.B. Brazdil (Eds.), Discovery Science (DS 2009), Lecture Notes in Artificial Intelligence, Vol. 5808, Springer (2009), 302-316.

• Masahiro Kimura, Kazumi Saito, and Hiroshi Motoda: Efficient estimation of influence functions for SIS model on social networks, In Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI-09), IJCAI (2009), 2046-2051.

• Masahiro Kimura, Kazumi Saito, Ryohei Nakano and Hiroshi Motoda: Finding influential nodes in a social network from information diffusion data, In H. Liu, J. Salerno and M. J. Young (Eds.), Social Computing and Behavioral Modeling, Springer (2009), 139-145.

• Ken-ichi Fukui, Kazuhisa Sato, Junichiro Mizusaki, Kazumi Saito, Masahiro Kimura and Masayuki Numao: Growth analysis of neighbor network for evaluation of damage progress, In T. Theeramunkong, B. Kijsirikul, N. Cercone, and T. Ho (Eds.), Advances in Knowledge Discovery and Data Minining (PAKDD 2009), Lecture Notes in Artificial Intelligence, Vol. 5476, Springer (2009), 933-940.

• Takayasu Fushimi, Takashi Kawazoe, Kazumi Saito, Masahiro Kimura and Hiroshi Motoda: What does an information diffusion model tell about social network structure?, In R. Debbie and K. Byeong-Ho (Eds.), Knowledge Acquisition: Approaches, Algorithms and Applications (PKAW 2008), Lecture Notes in Artificial Intelligence, Vol. 5465, Springer (2009), 122-136.

• Masahiro Kimura, Kazumi Saito and Hiroshi Motoda: Solving the contamination minimization problem on networks for the linear threshold model, In T. Ho and Z. Zhou (Eds.), PRICAI 2008: Trends in Artificial Intelligence, Lecture Notes in Artificial Intelligence, Vol. 5351, Springer (2008), 977-984.

• Kazumi Saito, Masahiro Kimura and Hiroshi Motoda: Effective visualization of information diffusion process over complex networks, In W. Daelemans, B. Goethals and K. Morik (Eds.), Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2008), Lecture Notes in Artificial Intelligence, Vol.5212, Springer (2008), 326-341.

• Kazumi Saito, Ryohei Nakano and Masahiro Kimura: Prediction of information diffusion probabilities for independent cascade model, In I. Lovrek, R. J. Howlett and L. C. Jain (Eds.), Knowledge-Based Intelligent Information and Engineering Systems (KES 2008), Lecture Notes in Artificial Intelligence, Vol.5179, Springer (2008), 67-75.

• Masahiro Kimura, Kazumi Saito and Hiroshi Motoda: Minimizing the spread of contamination by blocking links in a network, In Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI-08), AAAI (2008), 1175-1180.

• Ken-ichi Fukui, Kazumi Saito, Masahiro Kimura and Masayuki Numao: Sequence-based SOM: Visualizing transition of dynamic clusters, In Proceedings of the 8th IEEE International Conference on Computer and Information Technology (CIT 2008), IEEE (2008), 47-52.

• Masahiro Kimura, Kazumasa Yamakawa, Kazumi Saito and Hiroshi Motoda: Communiy analysis of influential nodes for information diffusion on a social network, In Proceedings of 2008 International Joint Conference on Neural Networks (IJCNN 2008), IEEE (2008), 1359-1364.

• Masahiro Kimura, Kazumi Saito and Ryohei Nakano: Extracting influential nodes for information diffuision on a social network, In Proceedings of the 22nd AAAI Conference on Artificial Intelligence (AAAI-07), AAAI (2007), 1371-1376.

• Kazumi Saito, Ryohei Nakano and Masahiro Kimura: Prediction of link attachments by estimating probabilities of information propagation, In B. Apolloni, R. J. Howlett and L. Jain (Eds.), Knowledge-Based Intelligent Information and Engineering Systems: KES 2007 - WIRN 2007, Lecture Notes in Artificial Intelligence, Vol.4694, Springer (2007), 235-242.

• Ken-ichi Fukui, Kazumi Saito, Masahiro Kimura and Masayuki Numao: Interpretable likelihood for vector representation topic, In B. Apolloni, R. J. Howlett and L. Jain (Eds.), Knowledge-Based Intelligent Information and Engineering Systems: KES 2007 - WIRN 2007, Lecture Notes in Artificial Intelligence, Vol.4694, Springer (2007), 202-209.

• Masahiro Kimura and Kazumi Saito: Tractable models for information diffusion in social networks, In J. Furnkrantz, T. Scheffer and M. Spilioulou (Eds.), Knowledge Discovery in Databases: PKDD 2006, Lecture Notes in Artificial Intelligence, Vol.4213, Springer (2006), 259-271.

• Masahiro Kimura and Kazumi Saito: Approximate solutions for the influence maximization problem in a social network, In B. Gabrys, R. J. Howlett and L. C. Jain (Eds.), Knowledge-Based Intelligent Information and Engineering Systems (KES 2006), Lecture Notes in Artificial Intelligence, Vol.4252, Springer (2006), 937-944.

• Ken-ichi Fukui, Kazumi Saito, Masahiro Kimura and Masayuki Numao: Visualization architecture based on SOM for two-lass sequential data, In Knowledge-Based Intelligent Information and Engineering Systems (KES 2006), Lecture Notes in Artificial Intelligence, Vol.4252, Springer (2006), 929-936.

• Kazumi Saito, Naonori Ueda, Masahiro Kimura, Kazuhiro Kazama and Shin-ya Sato: Filtering search engine spam based on anomaly detection approach, In Proceedings of the KDD2005 Workshop on Data Mining Methods for Anomaly Detection, (2005), 62-66.

• Masahiro Kimura, Kazumi Saito, Kazuhiro Kazama and Shin-ya Sato: Detecting search engine spam from a trackback network in blogspace, In R. Khosla, R. J. Howlett and L. C. Jain (Eds.), Knowledge-Based Intelligent Information and Engineering Systems (KES 2005), Lecture Notes in Artificial Intelligence, Vol.3684, Part IV, Springer (2005), 723-729.

• Ken-ichi Fukui, Kazumi Saito, Masahiro Kimura and Masayuki Numao: Visualizing dynamics of the hot topics using sequence-based self-orgnizing maps, In R. Khosla, R. J. Howlett and L. C. Jain (Eds.), Knowledge-Based Intelligent Information and Engineering Systems (KES 2005), Lecture Notes in Artificial Intelligence, Vol.3684, Part IV, Springer (2005), 745-751.

• Masahiro Kimura, Kazumi Saito and Naonori Ueda: Multinomial PCA for extracting major latent topics from document streams, In Proceedings of 2005 International Joint Conference on Neural Networks (IJCNN 2005), IEEE (2005), 238-243.

• Masahiro Kimura, Kazumi Saito and Naonori Ueda: Modeling share dynamics by extracting competition structure, In Proceedings of the 5th International Conference on Complex Systems, InterJournal, 2004, Manuscript Number 835.

• Masahiro Kimura, Kazumi Saito and Naonori Ueda: Modeling of growing networks with directional attachment and communities, In Proceedings of the 11th European Symposium on Artificial Neural Networks (ESANN 2003), (2003) 15-20.

• Masahiro Kimura, Kazumi Saito and Naonori Ueda: Modeling share dynamics by extracting competition structure, In Proceedings of the International Conference on Science of Modeling, ISM Report on Research and Education, No.17, (2003), 366-367.

• Masahiro Kimura, Kazumi Saito and Naonori Ueda: Modeling of growing networks with communities, In Proceedings of 2002 International Workshop on Neural Networks for Signal Processing (NNSP 2002), IEEE (2002), 189-198.

• Masahiro Kimura and Ryohei Nakano: Simplifying fully recurrent neural networks that have learned dynamical systems, In Proceedings of the 3rd International Conference on Computational Intelligence and Neuroscience, JCIS'98 Proceeedings, Vol.II, Association for Intelligent Machinery (1998), 143-146.

• Masahiro Kimura and Ryohei Nakano: Unique representations of dynamical systems produced by recurrent neural networks, In W. Gerstner, A. Germond, M. Hasler and J. D. Nicoud (Eds.), Artificial Neural Networks - ICANN'97, Lecture Notes in Computer Science, Vol.1327, Springer (1997), 403-408.

• Masahiro Kimura and Ryohei Nakano: Learning dynamical systems produced by recurrent neural networks, In C. von der Malsburg, W. von Seelen, J. C. Vorbruggen and B. Sendhoff (Eds.), Artificial Neural Networks - ICANN 96, Lecture Notes in Computer Science, Vol.1112, Springer (1996), 133-138.

• Masahiro Kimura and Ryohei Nakano: Learning dynamical systems from trajectories by continuous time recurrent neural networks, In Proceedings of 1995 International Conference on Neural Networks, IEEE (1995), 2992-2997.

• Masahiro Kimura, Takafumi Saito and Mikio Shinya: Surface deformation with differential geometric structures, In Abstracts of 3rd SIAM Conference on Geometric Design, Arizona, USA, SIAM (1993).

解説等:

• Masahiro Kimura: Growth models of web networks, NTT Technical Review, Vol.3, No.3, (2005), 24-27.

• 木村昌弘: 第6回情報論的学習理論ワークショップ(IBIS2003), 応用数理, Vol.14, No.2, (2004), 岩波書店, 91-92.

• 木村昌弘: Webの成長モデル, NTT技術ジャーナル, Vol.16, No.6, (2004), 18-21.

• 木村昌弘: 編集後記, 応用数理, Vol.14, No.3, (2004), 岩波書店, 85-86.

招待講演

• 木村昌弘: コミュニティー構造を考慮したWebの成長モデル, 第1回「ネットワーク生態系と空間デザイン」シンポジウム, 東京工科大学, 2003年3月28日.

• 木村昌弘: Webサイトの競合ダイナミックス, 第2回「ネットワーク生態系と空間デザイン」シンポジウム, ATR人間情報科学研究所, 2004年3月5日.