linkcomm

linkcomm identifies and analyzes link communities within biological networks to reveal overlapping community structure and node-level roles.

Key Features:

• Link-Based Clustering Algorithm: Clusters links rather than nodes to allow nodes to participate in multiple overlapping or nested communities.
• Support for Weighted and Directed Networks: Handles weighted and directed network data.
• Visualization Methods: Provides multiple visualization methods to represent link communities and their interrelationships.
• Downstream Analysis and Centrality Measures: Offers functions for downstream analysis, including node centrality measures derived from community structure.
• C++-implemented Core Algorithm for Scalability: Implements the primary algorithm in C++ to improve performance and scalability for large networks.
• Customizable Clustering Options: Includes several clustering methods with options to adjust the number of communities and to select methods based on memory constraints.

Scientific Applications:

• Protein–protein interaction networks: Identify overlapping functional modules within protein–protein interaction networks.
• Gene regulatory networks: Elucidate regulatory modules and highlight key regulatory elements in gene regulatory networks.
• Metabolic pathways: Reveal pathway modules and the dynamics of complex biological processes in metabolic pathway analysis.

Methodology:

Performs link-based clustering of network edges (links) rather than nodes, supports weighted and directed networks, uses a C++-implemented core algorithm, provides multiple clustering methods with adjustable community number and memory-dependent method selection, and includes visualization and downstream analysis functions including community-based node centrality measures.