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# Graph annotation generative adversarial networks

*Proceedings of The 14th Asian Conference on Machine Learning*, PMLR 189:16-16, 2023.

#### Abstract

We consider the problem of modelling
high-dimensional distributions and generating new
examples of data with complex relational feature
structure coherent with a graph skeleton. The model
we propose tackles the problem of generating the
data features constrained by the specific graph
structure of each data point by splitting the task
into two phases. In the first it models the
distribution of features associated with the nodes
of the given graph, in the second it complements the
edge features conditionally on the node features. We
follow the strategy of implicit distribution
modelling via generative adversarial network (GAN)
combined with permutation equivariant message
passing architecture operating over the sets of
nodes and edges. This enables generating the feature
vectors of all the graph objects in one go (in 2
phases) as opposed to a much slower one-by-one
generations of sequential models, prevents the need
for expensive graph matching procedures usually
needed for likelihood-based generative models, and
uses efficiently the network capacity by being
insensitive to the particular node ordering in the
graph representation. To the best of our knowledge,
this is the first method that models the feature
distribution along the graph skeleton allowing for
generations of annotated graphs with user specified
structures. Our experiments demonstrate the ability
of our model to learn complex structured
distributions through quantitative evaluation over
three annotated graph datasets.