Generative models are commonly used in statistical pattern recognition to describe the probability distributions of patterns in a vector space.
The supergraph presented in this chapter is a generative model which can fulfil the tasks of graph classification, graph clustering, and of generating new ...
Generative models are commonly used in statistical pattern recognition to describe the probability distributions of patterns in a vector space.
Title, Graph Generative Models from Information Theory ; Author, Lin Han ; Published, 2012 ; Length, 132 pages.
In this paper we present a method for constructing a generative prototype for a set of graphs by adopting a minimum description length approach.
Dec 22, 2020 · Graph generation can be divided into two parts, identifying a good model that represents a graph, and how we generate a graph once we have a model.
This paper unifies the autoregressive and one-shot graph generation methods into a unified diffusion model, where the removal of nodes is used as the forward ...
We present a method for constructing a generative model for sets of graphs by adopting a minimum description length approach. The method is posed in terms ...
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Oct 16, 2015 · In this paper we present a method for constructing a generative prototype for a set of graphs by adopting a minimum description length ...
May 26, 2021 · For more information about Stanford's Artificial Intelligence ... The goal of generative models for graphs is to generate synthetic graphs ...