Modularity and community detection in Semantic Similarity Networks trough Spectral Based Transformation and Markov Clustering

Semantic Similarity Networks are currently used for modeling similarities among biological entities. Nodes of such networks are for instance proteins while weighted edges among them encode semantic similarity scores among them. Networks are usually affected by noise. This paper presents an algorithm for de-noising these networks. The improvement of the use of mining algorithm on processed networks is also shown.