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Sato, Yuji*; Tsukamoto, Masahiro*; Shobu, Takahisa; Funada, Yoshinori*; Yamashita, Yorihiro*; Hara, Takahiro*; Sengoku, Masanori*; Sakon, Yu*; Okubo, Tomomasa*; Yoshida, Minoru*; et al.
Applied Surface Science, 480, p.861 - 867, 2019/06
Times Cited Count：32 Percentile：82.47(Chemistry, Physical)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.
*; *; *; *; *; *; *; Oikawa, Toshihiro; *; *; et al.
Fusion Energy 1996, p.885 - 890, 1997/05
no abstracts in English
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JAERI-M 82-204, 96 Pages, 1982/12
no abstracts in English
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.
