Identifying robust communities and multi-community nodes by combining top-down and bottom-up approaches to clustering

Abstract

Biological functions are carried out by groups of interacting molecules, cells or tissues, known as communities. Membership in these communities may overlap when biological components are involved in multiple functions. However, traditional clustering methods detect non-overlapping communities. These detected communities may also be unstable and difficult to replicate, because traditional methods are sensitive to noise and parameter settings. These aspects of traditional clustering methods limit our ability to detect biological communities, and therefore our ability to understand biological functions. To address these limitations and detect robust overlapping biological communities, we propose an unorthodox clustering method called SpeakEasy which identifies communities using top-down and bottom-up approaches simultaneously. Specifically, nodes join communities based on their local connections, as well as global information about the network structure. This method can quantify the stability of each community, automatically identify the number of communities, and quickly cluster networks with hundreds of thousands of nodes. SpeakEasy shows top performance on synthetic clustering benchmarks and accurately identifies meaningful biological communities in a range of datasets, including: gene microarrays, protein interactions, sorted cell populations, electrophysiology and fMRI brain imaging.

References in corpus (11)

• Modularity and community structure in networks
• Maps of random walks on complex networks reveal community structure
• Near linear time algorithm to detect community structures in large-scale networks
• Benchmark graphs for testing community detection algorithms
• Resolution limit in community detection
• Finding statistically significant communities in networks
• Structure and dynamics of molecular networks: A novel paradigm of drug discovery. A comprehensive review
• Community Detection via Maximization of Modularity and Its Variants
• Towards real-time community detection in large networks
• Mixing local and global information for community detection in large networks
• A New Metric for Quality of Network Community Structure

Cited by in corpus (2)

• Label propagation for clustering
• A Synthetic Network Generator for Covert Network Analytics