bNeSiFC: The Boosted NeSiFC Algorithm for Fast Fuzzy Community Detection based on Neighbors’ Similarity

This paper reports a novel fuzzy community detection (FCD) algorithm, which we term as ‘Boosted NeSiFC (bNeSiFC)’, based on an improvement of the recently proposed NeSiFC approach. Similar to the basic NeSiFC approach, the proposed bNeSiFC also computes the similarity between two neighbors using the modified local random walk (mLRW). In the proposed bNeSiFC, a new similarity metric termed EDS is introduced to compute the pair-similarity for constructing the transition probability matrix of mLRW. The boosted NeSiFC outperforms over the basic NeSiFC in terms of a faster computation in finding the most similar neighbors through the incorporation of the newly proposed metric EDS. Also, we introduce a novel fuzzy membership degree computation method for the proposed bNeSiFC, which is much clearer and easy to interpret than the one used for the basic NeSiFC. Comparative analysis of the experimental results with eight different real-life datasets establishes the superiority of the bNeSiFC over the NeSiFC and other existing approaches.