An efficient method for network topology identification based on an SOM algorithm

2003 ◽  
Vol 142 (4) ◽  
pp. 34-44
Author(s):  
Koji Hosaka ◽  
Takeshi Goya ◽  
Daisuke Umehara ◽  
Makoto Kawai
Keyword(s):  
Network Topology ◽  
Efficient Method ◽  
Topology Identification ◽  
Som Algorithm

scholarly journals An Efficient Method for Network Topology Identification Based on SOM Algorithm

2002 ◽  
Vol 122 (2) ◽  
pp. 208-216
Author(s):  
Koji Hosaka ◽  
Takeshi Goya ◽  
Daisuke Umehara ◽  
Makoto Kawai
Keyword(s):  
Network Topology ◽  
Efficient Method ◽  
Topology Identification ◽  
Som Algorithm

scholarly journals Distribution Network Topology Identification Considering Nonsynchronous Multi-prosumer Data Measurement

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Lizong Zhang ◽  
Fengming Zhang ◽  
Xiaolei Li ◽  
Chunlei Wang ◽  
Taotao Chen ◽  
...  
Keyword(s):  
Network Topology ◽  
Distribution Network ◽  
Topology Identification ◽  
Data Measurement

scholarly journals Distribution network topology identification method based on matching loop power

2020 ◽  
Vol 1633 ◽  
pp. 012093
Author(s):  
Yaojun Chen ◽  
Jun Deng ◽  
Xiaochun Zhang ◽  
Jianjiang Zhao ◽  
Shenshen Feng ◽  
...  
Keyword(s):  
Network Topology ◽  
Distribution Network ◽  
Topology Identification ◽  
Identification Method

scholarly journals PROBING COMPLEX NETWORKS FROM MEASURED TIME SERIES

2012 ◽  
Vol 22 (10) ◽  
pp. 1250236 ◽  
Author(s):  
LIANG HUANG ◽  
YING-CHENG LAI ◽  
MARY ANN F. HARRISON
Keyword(s):  
Time Series ◽  
Complex Networks ◽  
Network Topology ◽  
Efficient Method ◽  
Relative Importance ◽  
Computationally Efficient ◽  
Heuristic Argument ◽  
Principal Eigenvector ◽  
Measured Time ◽  
Potential Applications

We propose a method to detect nodes of relative importance, e.g. hubs, in an unknown network based on a set of measured time series. The idea is to construct a matrix characterizing the synchronization probabilities between various pairs of time series and examine the components of the principal eigenvector. We provide a heuristic argument indicating the existence of an approximate one-to-one correspondence between the components and the degrees of the nodes from which measurements are obtained. The striking finding is that such a correspondence appears to be quite robust, which holds regardless of the detailed node dynamics and of the network topology. Our computationally efficient method thus provides a general means to address the important problem of network detection, with potential applications in a number of fields.

scholarly journals Network topology identification from spectral templates

2016 ◽  
Author(s):  
Santiago Segarra ◽  
Antonio G. Marques ◽  
Gonzalo Mateos ◽  
Alejandro Ribeiro
Keyword(s):  
Network Topology ◽  
Topology Identification

scholarly journals Network topology identification based on measured data

2015 ◽  
Author(s):  
Mahdi Zamanighomi
Keyword(s):  
Network Topology ◽  
Measured Data ◽  
Topology Identification

Efficient Method of Collecting Network Traces for Generating Network Topology

2018 ◽  
Vol 45 (12) ◽  
pp. 1319-1328
Author(s):  
Jinsoo Kim ◽  
Haengrok Oh
Keyword(s):  
Network Topology ◽  
Efficient Method

Maximum likelihood network topology identification from edge-based unicast measurements

2002 ◽  
Vol 30 (1) ◽  
pp. 11-20 ◽  
Author(s):  
Mark Coates ◽  
Rui Castro ◽  
Robert Nowak ◽  
Manik Gadhiok ◽  
Ryan King ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Network Topology ◽  
Topology Identification ◽  
Edge Based

Network topology identification from imperfect spectral templates

2016 ◽  
Author(s):  
Santiago Segarra ◽  
Antonio G. Marques ◽  
Gonzalo Mateos ◽  
Alejandro Ribeiro
Keyword(s):  
Network Topology ◽  
Topology Identification
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