COMMUNITY DISTRIBUTION ON WEIGHTED SOCIAL NETWORKS AND TIME RESOLUTION

Abstract

The main aim of this paper is improving the efficiency and accuracy of community detection in complex networks. A popular method for detecting communities consists of maximizing a quality function known as modularity. We proposed a new algorithm, which is based on the idea that communities could be detected from subnetworks by comparing the internal and external density of each subnetwork. In our method, similar nodes are firstly gathered into meta-communities, which are then decided to be merged through a label propagation process, until all of them meet our community structure. An important meso-scale feature of these networks is measured though their community structure, which defines groups of strongly connected nodes that exist within and across network. Because subnetwork edges can describe relationships between different modalities, scales, or time points, it is essential to understand how communities change and evolve across them. Here, we expose an upper bound for time resolution beyond which community changes across layers cannot be detected. This upper bound has non-trivial, purely multilayer effects and acts as a resolution limit for detecting evolving communities. Our findings not only represent new theoretical considerations but also have important practical implications for choosing multilayer networks to model real-world systems whose communities change across time or modality. Our algorithm requires neither any priori information of communities nor optimization of any objective function. Experimental results show that, our algorithm performs quite well and runs extremely fast, compared with several other popular algorithms. By tuning time as a resolution parameter, we can also observe communities at different scales, so this could reveal the hierarchical structure of the network