  Graph generation is a fundamental problem in various domains, including
chemistry and social networks. Recent work has shown that molecular graph
generation using recurrent neural networks (RNNs) is advantageous compared to
traditional generative approaches which require converting continuous latent
representations into graphs. One issue which arises when treating graph
generation as sequential generation is the arbitrary order of the sequence
which results from a particular choice of graph flattening method. In this work
we propose using RNNs, taking into account the non-sequential nature of graphs
by adding an Orderless Regularization (OLR) term that encourages the hidden
state of the recurrent model to be invariant to different valid orderings
present under the training distribution. We demonstrate that sequential graph
generation models benefit from our proposed regularization scheme, especially
when data is scarce. Our findings contribute to the growing body of research on
graph generation and provide a valuable tool for various applications requiring
the synthesis of realistic and diverse graph structures.
