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人工知能学会論文誌
Vol. 29 (2014) No. 2 Special " Data Mining and Simulation ", Regular p. 234-244

記事言語:

http://doi.org/10.1527/tjsai.29.234

原著論文

We present a method for detecting community structures based on centrality value and node closeness. Many real world networks possess a scale-free property. This property makes community detection difficult especially on the widely used algorithms that are based on modularity optimization. However, in our algorithm, communities are formed from hub nodes. Thus communities with scale-free property can be identified correctly. The method does not contain any random element, nor requires pre-determined number of communities. Our experiments showed that our algorithm is better than algorithms based on modularity optimization in both real world and computer generated scale-free datasets.

Copyright © The Japanese Society for Artificial Intelligence 2014

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