The quality of network clustering is often measured in terms of a commonly used metric known as “modularity”. Modularity compares the clusters found in a network to those present in a random graph (a “null model”). Unfortunately, modularity is somewhat ill suited for studying spatially embedded networks, since a random graph contains no basic geometrical notions. Regardless of their distance, the null model assigns a nonzero probability for an edge to appear between any pair of nodes. Here, we propose a variant of modularity that does not rely on the use of a null model. To demonstrate the essentials of our method, we analyze networks generated from granular ensemble. We show that our method performs better than the most commonly used Newman-Girvan (NG) modularity in detecting the best (physically transparent) partitions in those systems. Our measure further properly detects hierarchical structures, whenever these are present.
CITATION STYLE
Kishore, R., Gogineni, A. K., Nussinov, Z., & Sahu, K. K. (2019). A nature inspired modularity function for unsupervised learning involving spatially embedded networks. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-39180-8
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