PaSca: a Graph Neural Architecture Search System under the Scalable Paradigm
Wentao Zhang,
Yu Shen,
Zheyu Lin
et al.
Abstract:Graph neural networks (GNNs) have achieved state-of-the-art performance in various graph-based tasks. However, as mainstream GNNs are designed based on the neural message passing mechanism, they do not scale well to data size and message passing steps. Although there has been an emerging interest in the design of scalable GNNs, current researches focus on specific GNN design, rather than the general design space, limiting the discovery of potential scalable GNN models. This paper proposes PaSca, a new paradigm… Show more
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