Permutation Invariant Graph Generation via Score-Based Generative Modeling

03/02/2020
by   Chenhao Niu, et al.
18

Learning generative models for graph-structured data is challenging because graphs are discrete, combinatorial, and the underlying data distribution is invariant to the ordering of nodes. However, most of the existing generative models for graphs are not invariant to the chosen ordering, which might lead to an undesirable bias in the learned distribution. To address this difficulty, we propose a permutation invariant approach to modeling graphs, using the recent framework of score-based generative modeling. In particular, we design a permutation equivariant, multi-channel graph neural network to model the gradient of the data distribution at the input graph (a.k.a., the score function). This permutation equivariant model of gradients implicitly defines a permutation invariant distribution for graphs. We train this graph neural network with score matching and sample from it with annealed Langevin dynamics. In our experiments, we first demonstrate the capacity of this new architecture in learning discrete graph algorithms. For graph generation, we find that our learning approach achieves better or comparable results to existing models on benchmark datasets.

READ FULL TEXT
01/26/2023

AlignGraph: A Group of Generative Models for Graphs

It is challenging for generative models to learn a distribution over gra...
08/30/2021

Adversarial Stein Training for Graph Energy Models

Learning distributions over graph-structured data is a challenging task ...
05/08/2019

PiNet: A Permutation Invariant Graph Neural Network for Graph Classification

We propose an end-to-end deep learning learning model for graph classifi...
02/15/2018

Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction

Structured prediction is concerned with predicting multiple inter-depend...
10/17/2019

Graph Embedding VAE: A Permutation Invariant Model of Graph Structure

Generative models of graph structure have applications in biology and so...
06/07/2023

Permutation Equivariant Graph Framelets for Heterophilous Semi-supervised Learning

The nature of heterophilous graphs is significantly different with that ...
12/07/2021

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