On spectral partitioning of co-authorship networks

Authors:
Václav Snášel;Pavel Krömer;Jan Platoš;Miloš Kudělka;Zdeněk Horák
Affiliations:
Department of Computer Science, VŠB-Technical University of Ostrava, Ostrava-Poruba, Czech Republic;Department of Computer Science, VŠB-Technical University of Ostrava, Ostrava-Poruba, Czech Republic;Department of Computer Science, VŠB-Technical University of Ostrava, Ostrava-Poruba, Czech Republic;Department of Computer Science, VŠB-Technical University of Ostrava, Ostrava-Poruba, Czech Republic;Department of Computer Science, VŠB-Technical University of Ostrava, Ostrava-Poruba, Czech Republic
Venue:
CISIM'12 Proceedings of the 11th IFIP TC 8 international conference on Computer Information Systems and Industrial Management
Year:
2012

Citing 12
Cited 0

Supervised Learning of Large Perceptual Organization: Graph Spectral Partitioning and Learning Automata

IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A tutorial on spectral clustering

Statistics and Computing
Spectral clustering in telephone call graphs

Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Statistical properties of community structure in large social and information networks

Proceedings of the 17th international conference on World Wide Web
An Efficient Spectral Algorithm for Network Community Discovery and Its Applications to Biological and Social Networks

ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Spectral Clustering in Social Networks

Advances in Web Mining and Web Usage Analysis
Spectral clustering based on the graph p-Laplacian

ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Clustering social networks

WAW'07 Proceedings of the 5th international conference on Algorithms and models for the web-graph
Revealing social networks of spammers through spectral clustering

ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Discrete Calculus: Applied Analysis on Graphs for Computational Science

Discrete Calculus: Applied Analysis on Graphs for Computational Science

Quantified Score

Hi-index	0.00

Visualization

Abstract

Spectral partitioning is a well known method in the area of graph and matrix analysis. Several approaches based on spectral partitioning and spectral clustering were used to detect structures in real world networks and databases. In this paper, we explore two community detection approaches based on the spectral partitioning to analyze a co-authorship network. The partitioning exploits the concepts of algebraic connectivity and characteristic valuation to form components useful for the analysis of relations and communities in real world social networks.