![]() We find that, for these networks, DCD algorithms that utilize interlayer links to perform community carryover between layers outperform other methods. Here, we compare the performance of different DCD algorithms on functional temporal networks built from synthetic neuronal time series data with known community structure. In functional temporal networks derived from neuronal spike train data, communities are expected to be transient, and it is common for the network to contain multiple singleton communities. How to choose the most appropriate algorithm generally depends on the type of network being analyzed and the specific properties of the data that define the network. This is just how I tend to use Singletons and static (method filled) classes, not sure if it confirms to any "official" standards.Dynamic community detection (DCD) in temporal networks is a complicated task that involves the selection of an algorithm and its associated parameters. I never initialize a utility class, I just use the static methods in it for all repetitive methods. and then returns the result, without using any external (or at least no non-static) fields or methods. It's more like a wrapper for the static methods in it.Īn example of this that I use frequently is a utility class with methods that do repetitive tasks that take parameters, does a calculation with them/formats them/etc.
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