Paper Info
Title
Representativeness in Unweighted Networks Based on Local Dependency
Abstract
Structures of real-world networks show varying degrees of importance of the nodes in their surroundings. The topic of evaluating the importance of the nodes offers many different approaches. We present simple and straightforward approach for the evaluation of the nodes in undirected unweighted networks. The approach is based on x-representativeness measure which is originally intended for weighted networks. The x-representativeness takes into account the degree of the node and its nearest neighbors. Experiments with different real-world unweighted networks are presented. To apply the presented method it is necessary to transform undirected unweighted network into weighted network. Weights in our experiments are measured by dependency between adjacent nodes. The aim of these experiments is to show that the x-representativeness can be used to deterministically reduce the unweighted network to differently sized samples of representatives, while maintaining topological properties of the original network.
Year
DOI
Venue
2014
10.1109/INCoS.2014.68
Intelligent Networking and Collaborative Systems
Keywords
Field
DocType
computational complexity,graph theory,large-scale systems,network theory (graphs),computational complexity,local dependency,node importance degree evaluation,real-world unweighted network structures,topological properties,undirected unweighted network,undirected unweighted networks,weighted network,x-representativeness measure,complex networks,dependency,graph reduction,representativeness,sampling,social network analysis
Computer science,Representativeness heuristic,Social network analysis,Theoretical computer science,Weighted network,Sampling (statistics),Complex network,Artificial intelligence,Graph reduction,Machine learning,Distributed computing
Conference
Citations 
PageRank 
References 
0
0.34
7
Authors
3
Name
Order
Citations
PageRank
Milos Kudelka111623.81
Sarka Zehnalova284.83
Jan Platos328658.72