Faculty Profiles - KAZAMA Kazuhiro

Published Papers - KAZAMA Kazuhiro

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Clustering and visualizing functionally similar regions in large-scale spatial networks

Takayasu Fushimi, Kazumi Saito, Tetsuo Ikeda, Kazuhiro Kazama

Journal of Information Processing ( Information Processing Society of Japan ) 25 ( 6 ) 398 - 406 2017 [Refereed]

We address the problem of extracting functionally similar regions in urban streets and regard such regions as spatial networks. For this purpose, based on our previous algorithm called the FCE method that extracted functional clusters for each network, we propose a new method that efficiently deals with several large-scale networks by accelerating our previous algorithm using lazy evaluation and pivot pruning techniques. Then we present our new techniques for simultaneously comparing the extracted functional clusters of several networks and an effective way of visualizing these clusters by focusing on the fact that the maximum degree of the nodes in spatial networks is restricted to relatively small numbers. In our experiments using urban streets extracted from the OpenStreetMap data of four worldwide cities, we show that our proposed method achieved a reasonably high acceleration performance. Then we show that the functional clusters extracted by it are useful for understanding the properties of areas in a series of visualization results and empirically confirm that our results are substantially different from those obtained by representative centrality measures. These region characteristics will play important roles for developing and planning city promotion and travel tours as well as understanding and improving the usage of urban streets.

DOI

Proposal of Recipe Search Method by Temporal Representations Based on Temporal Features Derived from Recipe Usage History

桐本宙輝, 風間一洋

情報処理学会論文誌：データベース 9 ( 4 ) 11 - 16 2016.12 [Refereed]

本論文では，料理レシピのような永続性が高い情報をユーザが利用する際の時間特性を利用して，指定された時間的状況下で調理されたレシピを，時間表現で検索するための新しい手法を提案する．まず，ユーザが入力した時間的状況を表す言語表現を，それから導かれる時間パラメータと確率密度関数を用いて時間特性を表す特徴ベクトルに変換し，cookpadの「つくれぽ」の頻度変化から求めた特徴ベクトルとのコサイン類似度を求めることで，ユーザが想定している時間的状況で調理されるレシピを発見する．実際に，入力した時間表現とレシピの特徴ベクトルとの類似度が高いレシピを求めて，レシピの内容と特徴ベクトルの確率密度分布の一致度，類似度の値を分析して，入力時に想定した時間的状況によく合致しているレシピが得られることを示す．This paper presents a new method to search recipes, which are cooked under the specified temporal situation, by temporal representations. The method uses temporal features derived from recipe usage history. First, we convert an input language representation about a temporal situation into a temporal feature vector by a probability density function and temporal parameters. Next, we find temporally similar recipes by calculating the cosine similarity between an input feature vector and each recipe feature vector that is derived from frequency variation of cookpad's "Tsukurepo". We analyze the content of recipes, the degree of coincidence between feature vectors, and the values of similarities. In the result, we show that our proposed method is able to search recipes, which are appropriate to user input temporal situation.

ピボットを用いたK-medoidsクラスタリング高速化による大規模機能コミュニティ抽出

伏見卓恭, 斉藤和巳, 池田哲夫, 風間一洋

第15回情報科学技術フォーラム (FIT2016) ( 第15回情報科学技術フォーラム (FIT2016) ) 2016.09 [Refereed]

マルチエージェント型情報拡散モデルの提案

池田圭佑, 岡田佳之, 鳥海不二夫, 榊剛史, 風間一洋, 野田五十樹, 篠田孝祐, 諏訪博彦, 栗原聡

人工知能学会論文誌 31 ( 1 ) NFC-C_1 - 13 2016.09 [Refereed]

Functional Cluster Extraction from Large Spatial Networks

Takayasu Fushimi, Kazumi Saito, Tetsuo Ikeda, Kazuhiro Kazama

The 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM2016) ( IEEE Computer Society ) 57 - 62 2016.08 [Refereed]

DOI

Extracting and Characterizing Functional Communities in Spatial Networks

Takayasu Fushimi, Kazumi Saito, Tetsuo Ikeda, Kazuhiro Kazama

The Workshop on Artificial Intelligence for Tourism (AI4Tourism 2016) 2016.08 [Refereed]

複雑ネットワークの機能コミュニティ構造の可視化

FUSHIMI Takayasu, SAITO Kazumi, KAZAMA Kazuhiro

Journal of the Visualization Society of Japan ( The Visualization Society of Japan ) 36 ( 141 ) 68-74 - 20 2016.04 [Invited]

In this paper, in order to reveal the characteristics and functions of many networks with complex structure, we explain a framework that extracts communities with different point of view and colors all the nodes of visualization results according to the extracted communities. In real networks, each node has intrinsic functions and roles, and mutually affects on other nodes. In this paper, different from existing community of densely connected nodes, we extract the nodes with similar functions, referred to as functional community. Concretely, we calculate the PageRank score convergence curve of each node and divide all nodes by similarities of these curves as functional community. Finally we color all the node with respect to functional community. From experimental results using artificial and real networks, we confirm that our framework can extract adequate functional communities.

DOI

Proposal of AIDM: Agent-Based information diffusion model

Keisuke Ikeda, Takeshi Saskaki, Fujio Toriumi, Kazuhiro Kazama, Itsuki Noda, Hirohiko Suwa, Kosuke Shinoda, Satoshi Kurihara

Transactions of the Japanese Society for Artificial Intelligence ( Japanese Society for Artificial Intelligence ) 31 ( 1 ) 2016.01 [Refereed]

During the 2011 East Japan Great Earthquake Disaster, some people used social media such as Twitter to get information important to their lives. However, the spread of groundless rumor information was big social problem. Therefore, social media users pay attention to prevent wrong information from diffusing. The way to stop the spread of a false rumor is needed, so we have to understand a diffusion of information mechanism. We have proposed information diffusion model which is based on SIR model until now. This model is represented by the stochastic state transition model for whether to propagate the information, and its transition probability is defined as the same value for all agents. People ’s thinking or actions are not the same. To solve this problem, we adopted three elements in our model: A new internal state switching model, user diversity and multiplexing of information paths. In this paper, we propose a novel information diffusion model, the Agent-based Information Diffusion Model (AIDM). We reproduce two kinds of false rumor information diffusion using proposed model. One is “single burst type false rumor spread ”, and another is “multi burst type false rumor spread. ”Proposal model is estimated by comparing real data with a simulation result.

DOI

グラフ構造に着目した評価文書の極座標可視化法

伏見卓恭, 佐藤哲司, 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 ) 2016 ( 0 ) 1E41 - 1E41 2016

DOI

Content centrality measure for networks: Introducing distance-based Decay weights

Takayasu Fushimi, Tetsuji Satoh, Kazumi Saito, Kazuhiro Kazama, Noriko Kando

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ( Springer Verlag ) 10047 40 - 54 2016 [Refereed]

We propose a novel centrality measure that is called Content Centrality for a given network that considers the feature vector of each node generated from its posting activities in social media, its own properties and so forth, in order to extract nodes who have neighbors with similar features. We assume that nodes with similar features are located near each other and unevenly distributed over a network, and the density gradually or rapidly decreases according to the distance from the center of the feature distribution (node). We quantify the degree of the feature concentration around each node by calculating the cosine similarity between the feature vector of each node and the resultant vector of its neighbors with distance-based decay weights, then rank all the nodes according to the value of cosine similarities. In experimental evaluations with three real networks, we confirm the validity of the centrality rankings and discuss the relation between the estimated parameters and the nature of nodes.

DOI

Comparison of influence measures on structural changes focused on node functions

Takayasu Fushimi, Tetsuji Satoh, Kazumi Saito, Kazuhiro Kazama

17th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2015 - Proceedings ( Association for Computing Machinery, Inc ) 2015.12 [Refereed]

The structures of some real-world networks are dynamic in nature as time goes by. These changes consist of the addition or deletion of nodes or links and the rewiring of links. Even if link rewiring occurs, the influence degree tends to differ depending on the location in which it occurs, the nature of the nodes, and so forth. In this paper, by quantifying the influence degree of each node, we attempt to extract the influential structural changes wherein each node in a large population changes its function. Concretely, we de-fine the node function as the PageRank convergence curve of the node and the influence degree affecting the node as distance based on a correlation coefficient of convergence curves before and after change occurs. We then propose the Structural Change Influence Measure (SCIM), which is the average value of the influence degree of all nodes. Based on experimental evaluation using several synthetic and real networks, we found five promising properties of our proposed measure. Our method indicates a higher value for changes in: 1) number of link rewirings
2) concentrated link deleting
3) link addition between distant nodes
4) link addition between important and unimportant nodes
and 5) link deletion between communities.

DOI

距離減衰重みを導入したノード群への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

Webとデータベースに関するフォーラム (WebDB Forum 2015) ( 日本データベース学会 ) ( 2015 ) 142 - 149 2015.11 [Refereed]

時間減衰付きカテゴリ選択モデルを用いたレビュー傾向分析

伏見卓恭, 佐藤哲司, 斉藤和巳, 風間一洋

第14回情報科学技術フォーラム (FIT2015) ( 情報処理学会 ) - 2015.09 [Refereed]

多重有向グラフのコア部抽出のためのMDSR法の提案と評価

加藤翔子, 斉藤和巳, 風間一洋, 佐藤哲司

日本データベース学会和文論文誌 14-J ( 1 ) 2015.04 [Refereed]

ノード属性情報からのリンク存在確率の推定

伏見卓恭, 斉藤和巳, 風間一洋

日本データベース学会和文論文誌 ( 日本データベース学会 ) 13-J ( 2 ) 32 - 37 2015.02 [Refereed]

実距離を考慮した中心性指標の提案と重要観光スポット抽出への応用

伏見卓恭, 斉藤和巳, 武藤伸明, 池田哲夫, 風間一洋

人工知能学会論文誌, ネットワークが創発する知能論文特集 30 ( 4 ) 32 - 37 2015.02 [Refereed]

Proposing Centrality Measures Considering Actual Distances and Applications to Extracting Important Sightseeing Spots

Fushimi Takayasu, Saito Kazumi, Mutoh Nobuaki, Ikeda Tetsuo, Kazama Kazuhiro

Transactions of the Japanese Society for Artificial Intelligence ( The Japanese Society for Artificial Intelligence ) 30 ( 6 ) 703 - 712 2015

In this paper, we propose novel centrality measures which extract important nodes from weighted networks like road networks where an actual distance is assigned over each link. Since the distances between nodes are not taken into consideration in traditional centrality measures like closeness and betweenness, there is a limit to an application to a real world problem for road networks with distances. Aiming at extracting important sightseeing spots so as to improve the convenience of tourists, we propose two measures considering actual distances, one is ``detour centrality which is an easiness measure of brief detour and the other is ``convenience centrality which is an accessibility measure based on traditional closeness and betweenness centrality respectively. Furthermore, when we extracting two or more nodes, there is a problem which these nodes are located in near places with each other only by extracting nodes with high rank of a centrality measure, since the whole balance is not taken into consideration. To overcome these shortcomings, we extend the above-mentioned centrality measures to ``set detour centrality and ``set convenience centrality and attempt to maximize the utility of all tourists over the target area by extracting set of nodes so as to maximize the values of these set centrality measures. In our experiments using two real sightseeing spot datasets, we show that our extended measures can extract an appropriate set of spots in terms of easiness of detour and accessibility, and these measures are robust to change of distances and emerging some outlier spots.

DOI

PageRank収束曲線を用いたコミュニティ特性の定量化

伏見卓恭, 斉藤和巳, 風間一洋, 佐藤哲司

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 ) 2015 ( 0 ) 1M41 - 1M41 2015

<p>本稿では，ネットワーク内のコミュニティと呼ばれるサブネットワークを対象とし，その特性を定量化することでコミュニティ間の構造的な類似度を定義する．具体的には，PageRank収束曲線を各ノードの特徴ベクトルとして表現し，各コミュニティに属するノードの特徴ベクトルを平均化することで，コミュニティの特性を定量化する．複数の構造の異なるネットワークに適用し提案指標の有効性を確認する</p>

DOI

Classification Method for Shared Information on Twitter Without Text Data

Seigo Baba, Fujio Toriumi, Takeshi Sakaki, Kosuke Shinoda, Satoshi Kurihara, Kazuhiro Kazama, Itsuki Noda

WWW'15 COMPANION: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB ( ASSOC COMPUTING MACHINERY ) 1173 - 1178 2015 [Refereed]

During a disaster, appropriate information must be collected. For example, victims and survivors require information about shelter locations and dangerous points or advice about protecting themselves. Rescuers need information about the details of volunteer activities and supplies, especially potential shortages. However, collecting such localized information is difficult from such mass media as TV and newspapers because they generally focus on information aimed at the general public. On the other hand, social media can attract more attention than mass media under these circumstances since they can provide such localized information. In this paper, we focus on Twitter, one of the most influential social media, as a source of local information. By assuming that users who retweet the same tweet are interested in the same topic, we can classify tweets that are required by users with similar interests based on retweets. Thus, we propose a novel tweet classification method that focuses on retweets without text mining. We linked tweets based on retweets to make a retweet network that connects similar tweets and extracted clusters that contain similar tweets from the constructed network by our clustering method. We also subjectively verified the validity of our proposed classification method. Our experiment verified that the ratio of the clusters whose tweets are mutually similar in the cluster to all clusters is very high and the similarities in each cluster are obvious. Finally, we calculated the linguistic similarities of the results to clarify our proposed method's features. Our method classified topic-similar tweets, even if they are not linguistically similar.

DOI

Estimating Network Structure from Anonymous Ego-centric Information

Takayasu Fushimi, Kazumi Saito, Kazuhiro Kazama

2014 Pacific Rim Knowledge Acquisition Workshop (PKAW 2014) ( Springer ) 236 - 245 2014.12 [Refereed]

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Published Papers - KAZAMA Kazuhiro