Autoregressive Models for Sequences of Graphs

Daniele Zambon, Daniele Grattarola, Lorenzo Livi, Cesare Alippi
2019 2019 International Joint Conference on Neural Networks (IJCNN)  
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
more » ... with four baselines on synthetic GGPs, denoting a significantly better performance on all considered problems.
doi:10.1109/ijcnn.2019.8852131 dblp:conf/ijcnn/ZambonGLA19 fatcat:5oxhw4cjmbdozhkx4g7yp6wyiq