Paper Info
Title
A mixed graph model for community detection
Abstract
A mixed graph theoretic model is proposed for finding communities in a social network. Information on the habits (shopping habits, free time activities) is considered to be known at least for part of the society. The presented model is based on applying parallelly a standard and a bipartite graph. Compared to previous methods, the introduced algorithm has the advantage of noise-tolerance and is suitable independently of the size of the clusters in the graph. Clusters in the dataset tend to form dense subgraphs in both graph models. The idea is to speed up cluster core mining by a modified MST algorithm. Noise in the dataset is defined as missing information on a person's habits. Clustering noisy data is done by using a bipartite graph and fuzzy membership functions. The proposed algorithm can be used for predicting the missing data estimated on the available information patterns. The presented mixed graph model might also be used for image processing tasks.
Year
DOI
Venue
2012
10.1504/IJIIDS.2012.049316
IJIIDS
Keywords
Field
DocType
missing information,mixed graph theoretic model,graph model,modified mst algorithm,bipartite graph,noisy data,mixed graph model,community detection,missing data,available information pattern,proposed algorithm,social networks,clustering
Data mining,Computer science,Fuzzy logic,Bipartite graph,Mixed graph,Artificial intelligence,Missing data,Cluster analysis,Clustering coefficient,Graph (abstract data type),Moral graph,Machine learning
Journal
Volume
Issue
Citations 
6
5
4
PageRank 
References 
Authors
0.43
11
2
Name
Order
Citations
PageRank
Anita Keszler192.17
Tamás Szirányi215226.92