  We propose TD-GEN, a graph generation framework based on tree decomposition,
and introduce a reduced upper bound on the maximum number of decisions needed
for graph generation. The framework includes a permutation invariant tree
generation model which forms the backbone of graph generation. Tree nodes are
supernodes, each representing a cluster of nodes in the graph. Graph nodes and
edges are incrementally generated inside the clusters by traversing the tree
supernodes, respecting the structure of the tree decomposition, and following
node sharing decisions between the clusters. Finally, we discuss the
shortcomings of standard evaluation criteria based on statistical properties of
the generated graphs as performance measures. We propose to compare the
performance of models based on likelihood. Empirical results on a variety of
standard graph generation datasets demonstrate the superior performance of our
method.
