Algorithms for spanning trees of unweighted networks

Spanning tree of a network or a graph is a subgraph connecting all the nodes with the minimum number of edges. Spanning tree retains the connectivity of a network and possibly other structural properties, and is one of the simplest techniques for network simplification or sampling, and for revealing its backbone or skeleton. The Prim's algorithm and the Kruskal's algorithm are well known algorithms for computing a spanning tree of a weighted network. In this paper, we study the performance of these algorithms on unweighted networks, and compare them to different priority-first search algorithms. We show that the distances between the nodes and the diameter of a network are best preserved by an algorithm based on the breadth-first search node traversal. The algorithm computes a spanning tree with properties of a balanced tree and a power-law node degree distribution. We support our results by experiments on synthetic graphs and more than a thousand real networks, and demonstrate different practical applications of computed spanning trees. We conclude that, if a spanning tree is supposed to retain the distances between the nodes or the diameter of an unweighted network, then the breadth-first search algorithm should be the preferred choice.