See the Tree Through the Lines: The Shazoo Algorithm -- Full Version --
This solves a theoretical gap in graph prediction for weighted trees, with applications in domains like network analysis, though it is incremental as it generalizes prior work on unweighted trees and weighted lines.
The authors tackled the problem of predicting nodes on weighted trees, introducing the Shazoo algorithm which achieves near-optimal performance, with experiments showing it gets very close to or better than less scalable methods on real-world datasets.
Predicting the nodes of a given graph is a fascinating theoretical problem with applications in several domains. Since graph sparsification via spanning trees retains enough information while making the task much easier, trees are an important special case of this problem. Although it is known how to predict the nodes of an unweighted tree in a nearly optimal way, in the weighted case a fully satisfactory algorithm is not available yet. We fill this hole and introduce an efficient node predictor, Shazoo, which is nearly optimal on any weighted tree. Moreover, we show that Shazoo can be viewed as a common nontrivial generalization of both previous approaches for unweighted trees and weighted lines. Experiments on real-world datasets confirm that Shazoo performs well in that it fully exploits the structure of the input tree, and gets very close to (and sometimes better than) less scalable energy minimization methods.