Efficient Representation Learning of Subgraphs by Subgraph-To-Node Translation

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-theart models without substantial performance decrease.

Motif Prediction with Graph Neural Networks

“…Such architectures capture substructures of an input graph to achieve expressivity beyond the 1-WL-test [256]. Some achieve this by learning subgraph representations [51], [148], [193], [204], [209], [230], [275], [282], which they subsequently transform to graph representations. Others predict the properties of subgraphs themselves.…”