Minimum spanning tree reconstruction using autoencoders

Minimum spanning trees are widely used graph structures used to find relationships between data organized in a graph. This work proposes a new approach based on neural networks, in particular, autoencoders, to extrapolate this tree from the dissimilarity representation of the data, seen as a noisy version of the minimum spanning tree. After many tests done on different network, the final results confirm the validity of the idea.