Limits...
Game theory and extremal optimization for community detection in complex dynamic networks.

Lung RI, Chira C, Andreica A - PLoS ONE (2014)

Bottom Line: We propose a novel approach based on game theory elements and extremal optimization to address dynamic communities detection.Thus, the problem is formulated as a mathematical game in which nodes take the role of players that seek to choose a community that maximizes their profit viewed as a fitness function.Numerical results obtained for both synthetic and real-world networks illustrate the competitive performance of this game theoretical approach.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, Forecasting and Mathematics, Babeş-Bolyai University, Cluj Napoca, Romania.

ABSTRACT
The detection of evolving communities in dynamic complex networks is a challenging problem that recently received attention from the research community. Dynamics clearly add another complexity dimension to the difficult task of community detection. Methods should be able to detect changes in the network structure and produce a set of community structures corresponding to different timestamps and reflecting the evolution in time of network data. We propose a novel approach based on game theory elements and extremal optimization to address dynamic communities detection. Thus, the problem is formulated as a mathematical game in which nodes take the role of players that seek to choose a community that maximizes their profit viewed as a fitness function. Numerical results obtained for both synthetic and real-world networks illustrate the competitive performance of this game theoretical approach.

Show MeSH
Boxplots ().Boxplots indicate that NEO-CDD is capable to detect and maintain the community structures throughout the 50 timestamps with very good NMI values even for .
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3935827&req=5

pone-0086891-g003: Boxplots ().Boxplots indicate that NEO-CDD is capable to detect and maintain the community structures throughout the 50 timestamps with very good NMI values even for .

Mentions: Both numerical values and box-plots for the average NMI values over the 10 independent runs for the synthetic datasets are presented in Tables 5, 6 and 7 (values 1 and 0 represent the exact results 1 and 0 with no rounding, unnecessary decimal points are omitted) and Figures 3, 4, and 5.


Game theory and extremal optimization for community detection in complex dynamic networks.

Lung RI, Chira C, Andreica A - PLoS ONE (2014)

Boxplots ().Boxplots indicate that NEO-CDD is capable to detect and maintain the community structures throughout the 50 timestamps with very good NMI values even for .
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3935827&req=5

pone-0086891-g003: Boxplots ().Boxplots indicate that NEO-CDD is capable to detect and maintain the community structures throughout the 50 timestamps with very good NMI values even for .
Mentions: Both numerical values and box-plots for the average NMI values over the 10 independent runs for the synthetic datasets are presented in Tables 5, 6 and 7 (values 1 and 0 represent the exact results 1 and 0 with no rounding, unnecessary decimal points are omitted) and Figures 3, 4, and 5.

Bottom Line: We propose a novel approach based on game theory elements and extremal optimization to address dynamic communities detection.Thus, the problem is formulated as a mathematical game in which nodes take the role of players that seek to choose a community that maximizes their profit viewed as a fitness function.Numerical results obtained for both synthetic and real-world networks illustrate the competitive performance of this game theoretical approach.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, Forecasting and Mathematics, Babeş-Bolyai University, Cluj Napoca, Romania.

ABSTRACT
The detection of evolving communities in dynamic complex networks is a challenging problem that recently received attention from the research community. Dynamics clearly add another complexity dimension to the difficult task of community detection. Methods should be able to detect changes in the network structure and produce a set of community structures corresponding to different timestamps and reflecting the evolution in time of network data. We propose a novel approach based on game theory elements and extremal optimization to address dynamic communities detection. Thus, the problem is formulated as a mathematical game in which nodes take the role of players that seek to choose a community that maximizes their profit viewed as a fitness function. Numerical results obtained for both synthetic and real-world networks illustrate the competitive performance of this game theoretical approach.

Show MeSH