Evaluation of the CoDA community detection algorithm based on directed network

Abstract

CoDA (Communities through Directed Affiliations) algorithm is a kind of community detection algorithm which can successfully detect 2-mode communities based on probability model. The F-measure criterion, for information retrieval, is adapted to the evaluation of CoDA algorithm in directed networks with overlapping communities or non-overlapping communities. The value of F1-measure in F-measure criterion can reflect whether CoDA algorithm performs well or not. The data sets used in the experiment is generated by the LFR Benchmark tool. The minimum number of nodes in data set is 100 and the maximum is 20 000, and evaluated experiment is conducted when every 100 nodes is added. The results show that CoDA algorithm performs well when the number of nodes is bellow 1 600. CoDA algorithm's performance becomes worse with the increase of the number of nodes, which proves the CoDA algorithm based on probability model is applicable to the community detection of small-scale networks.

Keywords

• theory of algorithm
• community detection
• CoDA algorithm
• directed networks
• evaluate
• F-measure