An a posteriori measure of network modularity

Area of Science:

• Ecology
• Network Science
• Computational Biology

Background:

• Network modularity is crucial for understanding ecological systems and their emergent properties.
• Existing modularity measures can yield varying results in terms of both modularity values and module composition.
• A need exists for a posteriori measures to evaluate the goodness-of-fit for different modularity optimization methods.

Purpose of the Study:

• To propose a novel, interpretable measure of network modularity.
• To establish a goodness-of-fit metric for modularity analyses.
• To apply the proposed measure to a diverse dataset of ecological networks.

Main Methods:

• A new modularity measure is defined as the ratio of within-module interactions to between-module interactions.
• The proposed measure was applied to 290 ecological networks, including host-parasite (bipartite) and predator-prey (unipartite) interaction networks.
• The interpretability and presentation of results for a broad audience were emphasized.

Main Results:

• The proposed modularity measure offers straightforward interpretation.
• Application to ecological networks revealed new insights into modularity and measurement techniques.
• The measure effectively quantifies the degree of segregation within network modules.

Conclusions:

• The proposed ratio of within-module to between-module interactions serves as an effective and interpretable goodness-of-fit measure for network modularity.
• This approach simplifies the assessment of modularity in ecological networks for both specialists and general audiences.
• The study highlights the importance of evaluating different modularity metrics for robust ecological network analysis.