Scalable Generative Models for Graphs with Graph Attention Mechanism
Graphs are ubiquitous real-world data structures, and generative models that approximate distributions over graphs and derive new samples from them have significant importance. Among the known challenges in graph generation tasks, scalability handling of large graphs and datasets is one of the most...
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Zusammenfassung: | Graphs are ubiquitous real-world data structures, and generative models that
approximate distributions over graphs and derive new samples from them have
significant importance. Among the known challenges in graph generation tasks,
scalability handling of large graphs and datasets is one of the most important
for practical applications. Recently, an increasing number of graph generative
models have been proposed and have demonstrated impressive results. However,
scalability is still an unresolved problem due to the complex generation
process or difficulty in training parallelization. In this paper, we first
define scalability from three different perspectives: number of nodes, data,
and node/edge labels. Then, we propose GRAM, a generative model for graphs that
is scalable in all three contexts, especially in training. We aim to achieve
scalability by employing a novel graph attention mechanism, formulating the
likelihood of graphs in a simple and general manner. Also, we apply two
techniques to reduce computational complexity. Furthermore, we construct a
unified and non-domain-specific evaluation metric in node/edge-labeled graph
generation tasks by combining a graph kernel and Maximum Mean Discrepancy. Our
experiments on synthetic and real-world graphs demonstrated the scalability of
our models and their superior performance compared with baseline methods. |
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DOI: | 10.48550/arxiv.1906.01861 |