Memory-Based Graph Networks

Autor:	Khasahmadi, Amir Hosein, Hassani, Kaveh, Moradi, Parsa, Lee, Leo, Morris, Quaid
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Computer Science - Neural and Evolutionary Computing Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Graph neural networks (GNNs) are a class of deep models that operate on data with arbitrary topology represented as graphs. We introduce an efficient memory layer for GNNs that can jointly learn node representations and coarsen the graph. We also introduce two new networks based on this layer: memory-based GNN (MemGNN) and graph memory network (GMN) that can learn hierarchical graph representations. The experimental results shows that the proposed models achieve state-of-the-art results in eight out of nine graph classification and regression benchmarks. We also show that the learned representations could correspond to chemical features in the molecule data. Code and reference implementations are released at: https://github.com/amirkhas/GraphMemoryNet Comment: ICLR 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2002.09518 Zobrazit plný text záznamu View this record from Arxiv