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Wu, H.-Y.*; Takahashi, Shigeo*; Miyamura, Hiroko; Ozahata, Satoshi*; Nakao, Akihiro*
Electronic Imaging, 2017(1), p.118 - 130, 2017/01
Extracting hierarchical structures from networks provides us with an effective means of visualizing them, especially when they contain complicated node connectivities such as those in traffic and distributed networks. This paper presents an algorithm for inferring such partial orders by optimizing the network hierarchies along flow paths that are given as input. We study several network examples to demonstrate the feasibility of the proposed approach including course dependency charts, railway networks, and P2P networks.
Wu, H.-Y.*; Takahashi, Shigeo*; Miyamura, Hiroko; Ozahata, Satoshi*; Nakao, Akihiro*
Journal of Imaging Science and Technology, 60(6), p.060407_1 - 060407_13, 2016/11
Times Cited Count：0 Percentile：0.02(Imaging Science & Photographic Technology)Extracting hierarchical structures from networks provides us with an effective means of visualizing them, especially when they contain complicated node connectivities such as those in traffic and distributed networks. This paper presents an algorithm for inferring such partial orders by optimizing the network hierarchies along flow paths that are given as input. We study several network examples to demonstrate the feasibility of the proposed approach including course dependency charts, railway networks, and P2P networks.
Miyamura, Hiroko; Hu, H.-Y.*; Yoshida, Masahiro*; Ozahata, Satoshi*; Nakao, Akihiro*; Takahashi, Shigeo*
Shingaku Giho, 112(463), p.577 - 582, 2013/03
This report presents a method for visualizing scale-free networks including Overlay networks as an typical example. Visualizing large-scale and complicated networks such as social networks has recently been very popular while conventional network visualization techniques cannot allow us to understand the topological structure of the scale-free networks. This is because the vertex degrees vary at an exponential rate in the scale-free network and thus special attention should be given when visualizing the network connectivity and analyzing the network traffic there. In this report, we employ the hierarchical representation of the scale-free network by referring to their vertex degrees and autonomous system relationships, so that we can clearly visualize the topological structure of the network in 3D space and retrieve the traffic paths over the network.
Miyamura, Hiroko; Ozahata, Satoshi*; Nakao, Akihiro*; Kawashima, Konosuke*; Teshima, Naoya*; Suzuki, Yoshio; Takemiya, Hiroshi
Zen NUA C&C Shisutemu Yuzakai Heisei-22-Nendo Rombunshu (CD-ROM), 14 Pages, 2011/02
Recently, construction of large-scale distributed file systems has become easier because of reduced hardware costs and the availability of large hard disks and faster networks. However, if the sizes and/or numbers become too large, occasionally even the data server manager cannot control the storage situation of the files. Therefore, we propose the concept of adaptive network graph display, in which the graph style can be changed according to the details of what the user details is observing, and construct a visualization system based on this concept. When observing data globally, the proposed system selectively displays information based on the clustering result, and when observing data locally, the system displays detailed information. This technique is a basic technology for computational science to achieve large-scale datasets handling and knowledge succession which is a problem in atomic energy related fields.
Miyamura, Hiroko; Ozahata, Satoshi*; Shinano, Yuji*; Miyashiro, Ryuhei*
Proceedings of 8th International Joint Conference on Computer Science and Software Engineering (JCSSE-2011), p.187 - 192, 2011/00
This paper proposes an adaptive visualization technique for representing a large-scale hierarchical dataset within a limited display space. A hierarchical dataset has nodes and links that reveal the parent-child relationship between the nodes. These nodes and links are described using graphics primitives. When the number of these primitives is large, it is difficult to recognize the structure of the hierarchical data because several primitives overlap within a display space. In order To overcome this difficulty, we propose an adaptive visualization technique for hierarchical datasets. The proposed technique selects an appropriate graph style according to the nodal density in each area.
Miyamura, Hiroko; Ozahata, Satoshi*; Nakao, Akihiro*; Kawashima, Konosuke*
Shingaku Giho, 110(190), p.103 - 108, 2010/09
In this paper, we propose a visualization technique for handling large-scale experiment datasets in distributed file system. Traditional network visualization techniques have problem that not all data can be displayed at the same time because the file-sharing network data is too large-scale. Therefore, we propose a concept of adaptive network graph display, in which the graph style can be changed according to the details of user's observation. We construct a visualization system based on this concept. When observing data globally, the proposed system selectively displays information based on the clustering result, and when observing data locally, the system displays detailed information. This technique is a basic technology for computational science to achieve large-scale datasets handling which is a problem in atomic energy related fields.
Miyamura, Hiroko; Shinano, Yuji*; Ozahata, Satoshi*
Kashika Joho Gakkai-Shi, 30(Suppl.1), p.273 - 276, 2010/07
We propose an adaptive visualization technique for representing a large-scale hierarchical dataset within limited display space. A hierarchical dataset has nodes and links showing the parent-child relationship between the nodes. These nodes and links are described using graphics primitives. When the number of these primitives is large, it is difficult to recognize the structure of the hierarchical data because many primitives are overlapped within a limited region. To overcome this difficulty, we propose an adaptive visualization technique for hierarchical datasets. The proposed technique selects an appropriate graph style according to the nodal density in each area.
Miyamura, Hiroko; Ozahata, Satoshi*; Nakao, Akihiro*; Kawashima, Konosuke*; Suzuki, Yoshio
Denki Gakkai Kenkyukai Shiryo, Sangyo Keisoku Seigyo Kenkyukai (IIC-10-85), p.27 - 32, 2010/03
We propose a network visualization system for a file-sharing network data-set. The network dataset consists of the information on nodes and links. As the number of links increases, the visualization of the network becomes challenging since the links usually cross over one another, which makes it hard to understand the structure of the network through the visualization. In order to present a network structure clearly and to identify keynodes that play important roles in the network, we propose a network visualization system. In this research, we propose additional techniques that support users searching the key node by using interactive functions such as filtering and node arranging.
Miyamura, Hiroko; Ozahata, Satoshi*; Nakao, Akihiro*; Kawashima, Konosuke*; Suzuki, Yoshio
Shingaku Giho, 109(448), p.357 - 362, 2010/03
We propose a visualization system for file-sharing network data. The file-sharing networks have some problems, for example: the contents distribution of the copyright infringement files, an increase in the amount of traffic, and so on. To handle these problems, techniques for observing the network state and controlling the file sharing are proposed. In addition, the proposed techniques realize effective control by giving retrieval control to the key nodes, which are important roles in the file-sharing network.
Miyamura, Hiroko; Ozahata, Satoshi*; Nakao, Akihiro*; Kawashima, Konosuke*; Suzuki, Yoshio
Shingaku Giho, 109(188), p.85 - 90, 2009/09
We propose a network visualization method for a large-scale file-sharing network dataset. The network dataset has nodes and links that represent the connection between two nodes. When the number of links is large, it is difficult to recognize the structure of the network data, because many links are crossed. In this context, for recognizing the structure and exploring the key nodes, we propose a link-less network visualization method using matrix based representation. In addition, we demonstrate the effectiveness of the proposed method by applying it to the file-sharing network log data.
Miyamura, Hiroko; Yoshida, Masahiro*; Ozahata, Satoshi*; Takahashi, Shigeo*; Nakao, Akihiro*; Kawashima, Konosuke*
no journal, ,
We propose a multilevel graph layout technique for visualizing large scale network datasets. Large-scale datasets are said to be difficult to analyze and visualize. However, with our technique, the datasets can be displayed in multistep. For instance the outline of network dataset is shown first, and when focused on a certain region, the detailed information is shown as well. By using this technique, users can observe a large scale dataset from the outline to the detail seamlessly, and therefore this is valuable for the structural analysis of network datasets.
Miyamura, Hiroko; Wu, H.-Y.*; Takahashi, Shigeo*; Ozahata, Satoshi*; Nakao, Akihiro*
no journal, ,
In recent years, file-sharing applications have been widely used, and demand for analysis of the log data from the file-sharing process has increased. Visualization techniques are effective for the analysis of such log data. In this study, we propose a new network graph visualization technique with new node layout method for analyzing the file-sharing process using geographic information and the network hierarchical optimization. We applied the proposed technique to a log data of a file-sharing application.
Wu, H.-Y.*; Takahashi, Shigeo*; Miyamura, Hiroko; Ozahata, Satoshi*; Nakao, Akihiro*
no journal, ,
In recent years, file-sharing applications have been widely used, and demand for analysis of the log data from the file-sharing process has increased. Visualization techniques are effective for the analysis of such log data. In this study, we propose a new node layout method for analyzing the file-sharing process using the network hierarchical optimization.
