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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 isdoi:10.1109/ijcnn.2019.8852131 dblp:conf/ijcnn/ZambonGLA19 fatcat:5oxhw4cjmbdozhkx4g7yp6wyiq