Abstract
Community structure detection has proven to be important in revealing the underlying organisation of complex networks. While most current analyses focus on static networks, the detection of communities in dynamic data is both challenging and timely. An analysis and visualisation procedure for dynamic networks is presented here, which identifies communities and sub-communities that persist across multiple network snapshots. An existing method for community detection in dynamic networks is adapted, extended, and implemented. We demonstrate the applicability of this method to detect communities in networks where individuals tend not to change their community affiliation very frequently. When stability of communities cannot be assumed, we show that the sub-community model may be a better alternative. This is illustrated through test cases of social and biological networks. A plugin for Gephi, an open-source software program used for graph visualisation and manipulation, named “DyCoNet”, was created to execute the algorithm and is freely available from https://github.com/juliemkauffman/DyCoNet.
Highlights
Community structure as a modular architecture is common in complex systems, where communities are defined as groups of nodes with dense intra-community edges and sparse intercommunity connections [1,2]
The application of DyCoNet to a dynamic protein interaction network is considered in more detail, in order to delineate the sub-community model in biological data where communities cannot be assumed to be stable
Small illustrative example This example serves to illustrate how to use DyCoNet as well as how to interpret the output generated by the plugin
Summary
Community structure as a modular architecture is common in complex systems, where communities are defined as groups of nodes with dense intra-community edges and sparse intercommunity connections [1,2]. Nodes in the same community have been found to share common properties or play similar roles in the organisation of the network [3], often corresponding to a functional unit in the system [4] The detection of such communities, known as modules, has proven important in the investigation of the underlying principles governing complex systems and has been a very active area of research over the past decade. Community structure has been considered primarily in the context of static networks, in reality complex systems are not static; entities and their interactions can be created or cease to exist, resulting in dynamic effects [5,6] It follows that a current challenge in community structure detection is the incorporation of temporal information into network modelling frameworks. Community structure detection methods have previously served to propose functionally coherent modules in static protein protein interaction (PPI) networks [8,9,10], the introduction of dynamic effects in terms of spatial, temporal and environmental conditions can result in more accurate mechanistic models [11,12]
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