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Abstract

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.