GrannGAN: Graph annotation generative adversarial networks

by   Yoann Boget, et al.

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.


page 1

page 2

page 3

page 4


Labeled Graph Generative Adversarial Networks

As a new way to train generative models, generative adversarial networks...

MolGAN: An implicit generative model for small molecular graphs

Deep generative models for graph-structured data offer a new angle on th...

LIC-GAN: Language Information Conditioned Graph Generative GAN Model

Deep generative models for Natural Language data offer a new angle on th...

House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation

This paper proposes a novel graph-constrained generative adversarial net...

Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning

Recent work on graph generative models has made remarkable progress towa...

A Robust and Generalized Framework for Adversarial Graph Embedding

Graph embedding is essential for graph mining tasks. With the prevalence...

Permutation Equivariant Generative Adversarial Networks for Graphs

One of the most discussed issues in graph generative modeling is the ord...

Please sign up or login with your details

Forgot password? Click here to reset