An Improved LFM Algorithm Based on Fitness Function and Community Similarity

Detecting community structures has become very important to help us understand the characteristics of the complex networks. Local Fitness Method (LFM) may generate some homeless nodes because of its backtracking step. Local Fitness Method Extension (LFMEX) was proposed to improve LFM by adopting a new fitness value of nodes that determines if the neighbor node should be joined into the expanding community. The new fitness value of nodes removes the backtracking step and considers the indirect relationship, direct relationship, exclusive relationship between the node and community. By locally optimizing the fitness function, the raw community partition is got, which may has some near duplicated communities. These duplicated communities reduce the modularity of community partition and need to be merged together. A new community similarity criteria is proposed to evaluate the similarity between two communities from the random graph based probability theory. It considered the difference between real edges and expected edges between communities. The community similarity measure can be used to merge similar communities efficiently and improve the modularity of final community partition. Experiments showed that the method improves modularity performance and normalized mutual information in community detection.

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