Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Wireless Personal Communications
Published in: Wireless Personal Communications 1/2018

06-02-2018

Deep Convolutional Neural Networks for Feature Extraction of Images Generated from Complex Networks Topologies

Authors: Ye Xu, Yun Chi, Ye Tian

Published in: Wireless Personal Communications | Issue 1/2018

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

To identify topology features of different complex network topology is essential in network science researches. Apart from traditional tools in doing such jobs such as power-law, a proved method of convolutional neural network (CNN) is introduced into this research field after we re-format the complex network topology adjacent matrix into an image. We design a CNN of overall 10 layers comprising convolutional layers, pooling layers and a softmax dense layer at last to extract relevant features and classify such features. Experiments show that the CNN models can effectively extract target features and result in an average accuracy rate of 95.65% in feature classification.
previous article Monitoring Data Management Information System for Securities Market
next article An Approach to Domain Generic IC with Acceleration of Multi-island GA

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Adamic, L. A., Huberman, B. A., Barabási, A. L., Albert, R., Jeong, H., & Bianconi, G. (2000). Power-law distribution of the world wide web. Science, 287(5461), 2115.CrossRef
2.
Coronel-Brizio, H. F., Hernández-Montoya, A. R., & Rodríguez-Achach, M. E. (2017). Tests of fit for the power-law distribution and some applications to the mexican stock market index relative changes. Advances & Applications in Statistics, 50(2), 123–136.CrossRef
3.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In NIPS.
4.
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., et al. (1989). Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4), 541–551.CrossRef
5.
6.
Dean, J., & Ghemawat, S. (2004). Mapreduce: Simplified data processing on large clusters. In OSDI’04: Proceedings of the 6th symposium on operating systems design & implementation, p. 10–18.
7.
Dean, J., & Ghemawat, S. (2010). Mapreduce: A flexible data processing tool. Communications of the ACM, 53(1), 72–77.CrossRef
8.
9.
Behnke, S. (2003). Hierarchical neural networks for image interpretation, Vol. 2766 of lecture notes in computer science. Springer, pp. 1–2.
10.
Ciresan, D. C., Meier, U., Masci, J., Gambardella, L. M., & Schmidhuber, J. (2011). Flexible, high performance convolutional neural networks for image classification. In International joint conference on artificial intelligence (Vol. 1, pp. 1237–1242).
11.
Simard, P. Y., Steinkraus, D., & Platt, J. C. (2003). Best practices for convolutional neural networks applied to visual document analysis. In Seventh international conference on document analysis and recognition (Vol. 1, pp. 958–963).
12.
Hochreiter, S., Bengio, Y., Frasconi, P., & Schmidhuber, J. (2001). Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. In S. C. Kremer & J. F. Kolen (Eds.), A field guide to dynamical recurrent neural networks. Hoboken: IEEE Press.
13.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In NIPS, pp. 1097–1105.
14.
Martins, M., Guimaraes, L. N. F., & Fonseca, L. M. G. (2002). Texture feature neural classifier for remote sensing image retrieval systems. In Computer Graphics and Image Processing.
Metadata
Title
Deep Convolutional Neural Networks for Feature Extraction of Images Generated from Complex Networks Topologies
Authors
Ye Xu
Yun Chi
Ye Tian
Publication date
06-02-2018
Publisher
Springer US
Published in
Wireless Personal Communications / Issue 1/2018
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-018-5445-7

Other articles of this Issue 1/2018

Wireless Personal Communications 1/2018 Go to the issue

OriginalPaper

A Big Data-Driven Approach to Catering O2O Modeling

OriginalPaper

Outlier Detection for Control Process Data Based on Improved ARHMM

OriginalPaper

A Meta-analysis of Satisfaction-Loyalty Relationship in E-Commerce: Sample and Measurement Characteristics as Moderators

OriginalPaper

Interest Points Detection in Image Based on Topology Features of Multi-level Complex Networks

OriginalPaper

Forecasting Citywide Traffic Congestion Based on Social Media

OriginalPaper

Condition Assessment Approach of Hydraulic Brake for Large Crane Based on State Estimation Algorithm