2017
DOI: 10.1109/tbdata.2016.2628725
|View full text |Cite
|
Sign up to set email alerts
|

A Framework for Community Detection in Large Networks Using Game-Theoretic Modeling

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
17
0

Year Published

2017
2017
2020
2020

Publication Types

Select...
8

Relationship

2
6

Authors

Journals

citations
Cited by 38 publications
(17 citation statements)
references
References 33 publications
0
17
0
Order By: Relevance
“…Newman et al proposed the network modularity evaluation function [19][20][21][22], which is defined as (11). Its physical meaning is the proportion of edges connecting two nodes of the same type in the network minus the expected value of the proportion of the edges arbitrarily connecting the two nodes under the same community structure.…”
Section: Using the Modularity Function To Evaluate The Results Of DIVmentioning
confidence: 99%
See 2 more Smart Citations
“…Newman et al proposed the network modularity evaluation function [19][20][21][22], which is defined as (11). Its physical meaning is the proportion of edges connecting two nodes of the same type in the network minus the expected value of the proportion of the edges arbitrarily connecting the two nodes under the same community structure.…”
Section: Using the Modularity Function To Evaluate The Results Of DIVmentioning
confidence: 99%
“…Its physical meaning is the proportion of edges connecting two nodes of the same type in the network minus the expected value of the proportion of the edges arbitrarily connecting the two nodes under the same community structure. The modularity function is usually expressed by (11).…”
Section: Using the Modularity Function To Evaluate The Results Of DIVmentioning
confidence: 99%
See 1 more Smart Citation
“…Based on the communities found in the previous interval, this algorithm adjusts the communities to which the vertices belong to incrementally, and gradually analyses the changes of the network, in order to avoid the clustering of the whole network. In [6], a new game-theoretic approach towards community detection in large-scale complex networks is introduced, which is based on modified modularity. This method was developed from a modified adjacency and modified Laplacian matrices, as well as neighbourhood similarity, which can classify a given network into dense communities.…”
Section: Related Workmentioning
confidence: 99%
“…Through these financial support, the following articles have been published [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42].…”
Section: Acknowledgementsmentioning
confidence: 99%