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With the recent explosion of publicly available biological data, the analysisof networks has gained significant interest. In particular, recent promisingresults in Neuroscience show that the way neurons and areas of the brain areconnected to each other plays a fundamental role in cognitive functions andbehaviour. Revealing pattern and structures within such an intricate volume ofconnections is a hard problem that has its roots in Graph and Network Theory.Since many real world situations can be modelled through networks, structuresdetection algorithms find application in almost every field of Science. Theseare NP-complete problems; therefore the generally used approach is throughheuristic algorithms. Here, we formulate the problem of finding structures innetworks of neurons in terms of a community detection problem. We introduce adefinition of community and we construct a statistics-based heuristic algorithmfor directed and weighted networks aiming at identifying overlappingbidirectional communities in large networks. We carry out a systematic analysisof the algorithm's performance, showing excellent results over a wide range ofparameters (successful detection percentages almost $100\%$ all the time).Also, we show results on the computational time needed and we suggest futuredirections on how to improve computational performance.

Detection of multiple and overlapping bidirectional communities within large, directed and weighted networks of neurons