Published Papers - KAZAMA Kazuhiro

Predicting the future citation counts for arXiv preprints immediately after publication

MAEYAMA Kosuke, KAZAMA Kazuhiro, YOSHIDA Mitsuo, SATO Sho, ONAMI Jyunichi

JSAI Technical Report, SIG-KBS ( The Japanese Society for Artificial Intelligence )  133   17 - 24   2024.12

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特集：「地理空間データを利活用した研究の展望」特集「地理空間データを利活用した研究の展望」にあたって

伏見 卓恭, 風間 一洋

人工知能 ( 一般社団法人 人工知能学会 )  39 ( 4 ) 442 - 443   2024.07

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Burst Characteristics Analysis of Altmetrics for Research Papers

Kazuhisa KOBAYASHI, Kazuhiro KAZAMA, Mitsuo YOSHIDA, Ikki OHMUKAI, Sho SATO, Marie KATSURAI

The IEICE Transactions D (Japanese Edition) ( The Institute of Electronics, Information and Communication Engineers )  J105-D ( 5 ) 310 - 321   2022.05  [Refereed]

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Analysis of Leading Communities Contributing to arXiv Information Distribution on Twitter

Kyosuke Shimada, Kazuhiro Kazama, Mitsuo Yoshida, Ikki Ohmukai, Sho Sato

WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology     2021.12  [Refereed]

インターネット上のユーザの行動データを用いた論文の普遍性の分析手法

小林 和央, 風間 一洋, 吉田 光男, 大向 一輝, 佐藤 翔

情報知識学会誌   30 ( 3 )   2020.10  [Refereed]

媒介度に基づく道路ネットワークのコミュニティ抽出法

Fushimi Takayasu, Saito Kazumi, Ikeda Tetsuo, Kazama Kazuhiro

Transactions of the Japanese Society for Artificial Intelligence ( The Japanese Society for Artificial Intelligence )  34 ( 5 ) F-wd12_1 - 11   2020.01  [Refereed]

　View Summary

In this study, we address the problem of detecting effective installation sites of signboards and identifying their influential zones for a given road network, under the setting that many residents can view them on their shortest paths to the destinations. To this end, based on a notion of group-betweenness centrality measure, we newly formalize this problem as a k-betweens problem and propose a community extraction method of road networks to identify the influential zones. In an existing method, the influential zone of each signboard is extracted by Voronoi tessellation against its installation site, which assumes that residents view the nearest signboard. In our proposed method, it is extracted by the proportion including the signboard on the shortest paths from the resident’s departure point to various destinations. From experimental evaluations using artificial and real road networks, we confirmed that our method can extract effective installation sites of signboards such as intersection nodes near the entrance of the express highway, and their influential zones as communities according to the positional relationships with arterial roadways. Furthermore, by computing the degree of antagonism among communities using the entropy of betweenness contribution rates, we can quantify the effectiveness of installing the same signboards for residents of areas where the influential zones of multiple signboards overlap.

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Community extraction method of road networks based on betweenness con-tribution

Takayasu Fushimi, Kazumi Saito, Tetsuo Ikeda, Kazuhiro Kazama

Transactions of the Japanese Society for Artificial Intelligence ( Japanese Society for Artificial Intelligence )  35 ( 1 )   2020

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In this study, we address the problem of detecting effective installation sites of signboards and identifying their influential zones for a given road network, under the setting that many residents can view them on their shortest paths to the destinations. To this end, based on a notion of group-betweenness centrality measure, we newly formalize this problem as a fc-betweens problem and propose a community extraction method of road networks to identify the influential zones. In an existing method, the influential zone of each signboard is extracted by Voronoi tessellation against its installation site, which assumes that residents view the nearest signboard. In our proposed method, it is extracted by the proportion including the signboard on the shortest paths from the resident's departure point to various destinations. From experimental evaluations using artificial and real road networks, we confirmed that our method can extract effective installation sites of signboards such as intersection nodes near the entrance of the express highway, and their influential zones as communities according to the positional relationships with arterial roadways. Further-more. by computing the degree of antagonism among communities using the entropy of betweenness contribution rates, we can quantify the effectiveness of installing the same signboards for residents of areas where the influential zones of multiple signboards overlap.

DOI

cookpadの献立データからのパターン抽出

奈須 日向太, 風間 一洋

第12回Webとデータベースに関するフォーラム(WebDB Forum 2019) ( 日本データベース学会 )    2019.09  [Refereed]

学術情報検索における閲覧論文の文献種別による分析

小林 和央, 風間 一洋, 吉田 光男, 大向 一輝, 佐藤 翔

第12回Webとデータベースに関するフォーラム(WebDB Forum 2019) ( 日本データベース学会 )    2019.09  [Refereed]

Estimating Node Connectedness in Spatial Network under Stochastic Link Disconnection Based on Efficient Sampling

Takayasu Fushimi, Kazumi Saito, Tetsuo Ikeda, Kazuhiro Kazama

Applied Network Science   4 ( 66 ) 66 - 24   2019.08  [Refereed]

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A New Group Centrality Measure for Maximizing the Connectedness of Network Under Uncertain Connectivity

Takayasu Fushimi, Kazumi Saito, Tetsuo Ikeda, Kazuhiro Kazama

The 7th International Conference on Complex Networks and Their Applications (COMPLEX NETWORKS 2018) ( Springer )    3-14 - 14   2018.12  [Refereed]

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Improving Approximate Extraction of Functional Similar Regions from Large-Scale Spatial Networks Based on Greedy Selection of Representative Nodes of Different Areas

Takayasu Fushimi, Kazumi Saito, Tetsuo Ikeda, Kazuhiro Kazama

Applied Network Science   3 ( 18 ) 43844 - 14   2018.07  [Refereed]

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Acceleration of Functional Cluster Extraction and Analysis of Cluster Affinity

Takayasu Fushimi, Kazumi Saito, Tetsuo Ikeda, Kazuhiro Kazama

Machine Learning Techniques for Online Social Networks, Lecture Notes in Social Networks (LNSN) Series     43852   2018.05  [Refereed]

リンク切断に頑健な連結中心性とその高速計算法

伏見 卓恭, 斉藤 和巳, 池田 哲夫, 風間 一洋

情報処理学会論文誌：数理モデル化と応用   11 ( 2 )   2018.01  [Refereed]

Fast Extraction Method of Functional Clusters from Large-Scale Spatial Networks Based on Transfer Learning

Takayasu Fushimi, Kazumi Saito, Tetsuo Ikeda, Kazuhiro Kazama

The 6th International Conference on Complex Networks and Their Applications (COMPLEX NETWORKS 2017) ( Springer )    1210-1222 - 1222   2017.12  [Refereed]

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ネットワーク上での特徴量分布を考慮したアノテーション付与法

伏見 卓恭, 佐藤 哲司, 斉藤 和巳, 風間 一洋

情報処理学会論文誌, 特集「ネットワーク科学」   58 ( 6 ) 1246 - 1257   2017.06  [Refereed]

ジオタグ付きツイートを用いた交通路の抽出法

谷 直樹, 風間 一洋, 榊 剛史, 吉田 光男, 斉藤 和巳

情報処理学会論文誌：データベース   10 ( 2 ) 31 - 41   2017.06  [Refereed]

メトリック空間における複数カテゴリに属するハイブリッドオブジェクト抽出法の提案

伏見 卓恭, 斉藤 和巳, 風間 一洋

情報処理学会論文誌, 特集「ネットワーク科学」   58 ( 6 ) 1258 - 1267   2017.06  [Refereed]

一般化ピボットによるL1距離に基づくK-medoidsクラスタリングの高速化

伏見 卓恭, 斉藤 和巳, 風間 一洋

人工知能学会全国大会論文集 ( 一般社団法人 人工知能学会 )  2017 ( 0 ) 3M11 - 3M11   2017

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<p>高次元ベクトルで表現されるデータの増大により，クラスタリング技術が注目を浴びている．ベクトル間の距離計算には多大な計算コストを要するため，L1距離に着目した一般化ピボットを利用し効率的なピボット枝刈りによる高速なK-medoidsアルゴリズムを提案する．実データを用いた評価実験により，クラスタリングの高速化を実現できていることを示す．</p>

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Accelerating greedy K-medoids clustering algorithm with L1 distance by pivot generation

Takayasu Fushimi, Kazumi Saito, Tetsuo Ikeda, Kazuhiro Kazama

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ( Springer Verlag )  10352   87 - 96   2017  [Refereed]

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With the explosive increase of multimedia objects represented as high-dimensional vectors, clustering techniques for these objects have received much attention in recent years. However, clustering methods usually require a large amount of computational cost when calculating the distances between these objects. In this paper, for accelerating the greedy K-medoids clustering algorithm with L1 distance, we propose a new method consisting of the fast first medoid selection, lazy evaluation, and pivot pruning techniques, where the efficiency of the pivot construction is enhanced by our new pivot generation method called PGM2. In our experiments using real image datasets where each object is represented as a high-dimensional vector and L1 distance is recommended as their dissimilarity, we show that our proposed method achieved a reasonably high acceleration performance.

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