Many complex systems can be described as networks exhibiting inner organization as communities of nodes. The identification of communities is a key factor to understand community-based functionality. We propose a family of measures based on the weighted sum of two dissimilarity quantifiers that facilitates efficient classification of communities by tuning the quantifiers’ relative weight to the network’s particularities. Additionally, two new dissimilarities are introduced and incorporated in our analysis. The effectiveness of our approach is tested by examining the Zachary’s Karate Club Network and the Caenorhabditis elegans reactions network. The analysis reveals the method’s classification power as confirmed by the efficient detection of intrapathway metabolic functions in C. elegans.
What is a complex system? In general terms, a complex system consists of a number of interacting 'members', 'elements' or 'agents', which have the potential to generate qualitatively new collective behaviour.
Everybody knows a computer is a machine made of metal and plastic, with microchip cores turning streams of electrons into digital reality.
A century from now, though, computers could look quite different. They might be made from neurons and chemical baths, from bacterial colonies and pure light, unrecognizable to our old-fashioned 21st century eyes.
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