# Autoregressive Models for Sequences of Graphs

## TL;DR

This paper introduces an autoregressive model for sequences of graphs with variable topology, using graph neural networks to predict future graphs, outperforming existing baselines on synthetic data.

## Contribution

It formalizes an autoregressive model for general graph families and employs GNNs to learn and predict graph sequences, a novel approach in this domain.

## Key findings

- Significantly better performance than baselines on synthetic graph data.
- Effective modeling of variable-topology graph sequences.
- Demonstrates the potential of GNNs in sequence prediction tasks.

## Abstract

This paper proposes an autoregressive (AR) model for sequences of graphs, which generalises traditional AR models. A first novelty consists in formalising the AR model for a very general family of graphs, characterised by a variable topology, and attributes associated with nodes and edges. A graph neural network (GNN) is also proposed to learn the AR function associated with the graph-generating process (GGP), and subsequently predict the next graph in a sequence. The proposed method is compared with four baselines on synthetic GGPs, denoting a significantly better performance on all considered problems.