Towards the Inference of Graphs on Ordered Vertices
Alexander Zien, Gunnar Raetsch and Cheng Soon Ong
Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
We propose novel methods for machine learning of structured output spaces. Specifically, we consider outputs which are graphs with vertices that have a natural order. We consider the usual adjacency matrix representation of graphs, as well as two other representations for such a graph: (a) decomposing the graph into a set of paths, (b) converting the graph into a single sequence of nodes with labeled edges. For each of the three representations, we propose an encoding and decoding scheme. We also propose an evaluation measure for comparing two graphs.