Development and Improvement of Network Reduction Algorithms for Multilayer Networks

Given the complexity of today's networks, performing data analysis requires reducing the network’s size into smaller manageable useful sizes. To the best of our knowledge, in the domain of multilayer networks, reducing the size of such networks while simultaneously preserving the features and the nature of the network has not been done before. This paper, for the first time, combines three separate single-layer network simplification methods to make a new method for reducing the size of multilayer networks in a way that preserves the fundamental features of the network. The three simplification algorithms are Path Simplification, Degree-based Node Selection, and Hair Reduction algorithms. A hybrid approach is used for combining these algorithms with modifications to support multilayer features. To reduce the multilayer network, these algorithms are applied to the network sequentially. Our proposed method is tested on four real-world datasets. Results of the comparison among the reduced and the original networks, show that the reduced networks maintain the main features while their analysis complexity is less than the original ones.