A new Similarity Based Adapted Louvain Algorithm (SIMBA) for active module identification in p-value attributed...

Area of Science:

• Computational Biology
• Network Science
• Bioinformatics

Background:

• Real-world networks, especially biological ones, possess complex structures and node attributes, posing challenges for analysis.
• Existing community detection algorithms often prioritize topological data, neglecting attribute-based similarities crucial for identifying functional subnetworks.

Purpose of the Study:

• To develop a novel scoring method for graph partitioning that incorporates attribute-based similarities.
• To adapt the Louvain algorithm for optimizing this new scoring function to detect functionally coherent communities.

Main Methods:

• A new similarity function for node attributes was developed.
• The Louvain algorithm was modified to optimize the proposed scoring function for graph partitioning.
• The approach was evaluated on artificial and real-world biological network datasets.

Main Results:

• The proposed method successfully identified communities that are both densely connected and functionally coherent.
• Experiments demonstrated the superiority of the new approach over existing state-of-the-art methods.
• The integration of topological and attribute-based information proved effective.

Conclusions:

• The novel approach offers a powerful tool for uncovering biologically meaningful modules in complex networks.
• This method provides deeper insights into intricate biological processes by considering both network structure and node attributes.