Efficient Representation Learning of Subgraphs by Subgraph-To-Node TranslationDownload PDF

02 Mar 2022, 12:21 (edited 22 Apr 2022)GTRL 2022 PosterReaders: Everyone
  • Keywords: Efficiency, Subgraphs, Graph Neural Networks
  • TL;DR: We propose Subgraph-To-Node translation to efficiently learn representations of subgraphs by coarsely translating subgraphs into nodes.
  • Abstract: A subgraph is a data structure that can represent various real-world problems. We propose Subgraph-To-Node (S2N) translation, which is a novel formulation to efficiently learn representations of subgraphs. Specifically, given a set of subgraphs in the global graph, we construct a new graph by coarsely transforming subgraphs into nodes. We perform subgraph-level tasks as node-level tasks through this translation. By doing so, we can significantly reduce the memory and computational costs in both training and inference. We conduct experiments on four real-world datasets to evaluate performance and efficiency. Our experiments demonstrate that models with S2N translation are more efficient than state-of-the-art models without substantial performance decrease.
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