Question

In community detection within biological networks, what does the modularity score represent regarding the internal edges of a defined cluster versus edges between clusters?

Accepted Answer

In community detection, the modularity score measures the strength of the division of a network into clusters, also known as communities or modules. A cluster is defined as a group of nodes that are more densely connected to each other than to the rest of the network. The modularity score represents the difference between the actual fraction of edges that fall within a defined cluster and the expected fraction of edges that would fall within that cluster if the edges were placed at random, while keeping the number of edges connected to each node, called the node degree, the same. A positive modularity score indicates that a cluster has more internal edges—links between nodes inside the same group—than would be expected by random chance. A score near zero indicates that the internal edge density is no different from what would be expected by a random network, meaning the defined cluster lacks clear separation. Conversely, if a cluster has a lower number of internal edges than expected by chance, the modularity score will be negative, suggesting that the network is not naturally partitioned into those specific groups. In biological networks, such as protein-protein interaction maps, a high modularity score reflects a functional organization where proteins within a cluster are more likely to participate in the same biological process because they share more interactions with each other than they do with proteins outside their community.

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In community detection within biological networks, what does the modularity score represent regarding the internal edges of a defined cluster versus edges between clusters?