| 大会名称 |
| 2020年 総合大会 |
| 大会コ－ド |
| 2020G |
| 開催年 |
|
2020
|
| 発行日 |
|
2020-03-03
|
| セッション番号 |
|
BS-1
|
| セッション名 |
|
In-Network Intelligence for Design, Management, and Control of Future Networks and Services
|
| 講演日 |
|
2020/3/20
|
| 講演場所(会議室等) |
|
工学部 講義棟１F 104講義室
|
| 講演番号 |
|
BS-1-22
|
| タイトル |
|
A Study on the Predictability of Network Robustness against Random Node Removal from Spectral Measures
|
| 著者名 |
|
○Kazuyuki Yamashita, Yuichi Yasuda, Ryo Nakamura, Hiroyuki Ohsaki,
|
| キーワード |
|
Network Robustness, Spectral Measures, Largest Cluster Component
|
| 抄録 |
|
In this paper, we investigate how effectively predictive metrics (spectral measures) can estimate the robustness of a network against random and adversary node removal. Our finding includes that, among five types of spectral measures, the effective resistance is most suitable for predicting the largest cluster component size under low node removal ratio, and that the predictability of the effective resistance is stable among different types of networks.
|
| 本文pdf |
|
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|
