Community Detection in Multimodal Data: A Similarity Network Perspective

TL;DR

This paper evaluates how different similarity network construction methods perform in multimodal biomedical data, highlighting the limitations of popular techniques like SNF and NEMO when faced with noisy or incomplete data.

Contribution

It provides a comparative analysis of similarity network fusion methods, revealing their sensitivities and robustness in multimodal data integration scenarios.

Findings

SNF and NEMO do not outperform simple mean similarity aggregation in certain conditions.

SNF is highly sensitive to incomplete modalities, unlike NEMO and mean aggregation.

Simple aggregation methods are more resilient to noise and missing data.

Abstract

Similarity network construction is a fundamental step in many approaches to community detection in biomedical analysis. It is utilised both in the creation of network structures from non-relational data and as a processing step in clustering pipelines. The foundation of any network analysis approach hinges on the quality of the underlying network. With the rising popularity of network learning and use of network-based clustering, the importance of correctly constructing the network is vital. The underlying mechanisms of similarity network construction, particularly the implications of the choice of approach for multi-modal integration, remain poorly explored. By introducing differences in embedded cluster information and noise levels across modalities, we assess the performance of popular similarity integration techniques such as Similarity Network Fusion (SNF) and NEighborhood based Multi-Omics clustering (NEMO). Notably, SNF and NEMO fail to outperform simpler techniques such as mean similarity aggregation when incorporating modalities with inconsistently embedded clusters. We demonstrate how integration methods can be used to incorporate partial modalities - datasets where not all individuals have a full set of measurements in all modalities. SNF shows significant sensitivity to incomplete modalities while NEMO and mean aggregation are more resilient.