AlignGraph: A Group of Generative Models for Graphs

This is a problem because state-of-the-art generative models, like the ones listed above, rely on latent It is challenging for generative models to learn a distribution node embeddings. Such embeddings vary drastically over graphs because of the lack of permutation even under nearly isomorphic graphs [13]. In turn, this invariance: nodes may be ordered arbitrarily across can hamper the fidelity of the graph generation process graphs, and standard graph alignment is combinatorial significantly. Note that this is a much harder setting and notoriously expensive. We propose AlignGraph, a than, e.g., images or text, where inputs have a canonical group of generative models that combine fast and efficient orientation. Finding the correspondence between graph graph alignment methods with a family of deep nodes is a notoriously hard problem [14, 15, 11, 16], and generative models that are invariant to node permutations.