Working Paper Article Version 1 This version is not peer-reviewed

Hierarchical Pooling in Graph Neural Networks to Enhance Classification Performance in Large Datasets

Version 1 : Received: 15 June 2021 / Approved: 15 June 2021 / Online: 15 June 2021 (14:32:53 CEST)

A peer-reviewed article of this Preprint also exists.

Pham, H.V.; Thanh, D.H.; Moore, P. Hierarchical Pooling in Graph Neural Networks to Enhance Classification Performance in Large Datasets. Sensors 2021, 21, 6070. Pham, H.V.; Thanh, D.H.; Moore, P. Hierarchical Pooling in Graph Neural Networks to Enhance Classification Performance in Large Datasets. Sensors 2021, 21, 6070.

Journal reference: Sensors 2021, 21, 6070
DOI: 10.3390/s21186070

Abstract

In considering knowledge graphs in a diverse range of domains of interest, graph neural networks have demonstrated significant improvements in node classification and prediction when applied to graph representation with learning node embedding to effectively represent hierarchical properties of graphs. DiffPool is a deep-learning approach using a differentiable graph pooling technique that generates hierarchical representations of graphs. In operation DiffPool is a differentiable graph pooling technique that generates hierarchical representations of graphs. DiffPool learns a differentiable soft cluster assignment for nodes at each layer of a deep graph neural network with nodes mapped sets of clusters. However, control of the learning process is difficult given the complexity and large number of parameters on an `end-to-end’ model. To address this difficulty we propose an novel approach termed FPool which is predicated on the basic approach adopted in DiffPool (where pooling is applied directly to node representations). Methods designed to enhance data classification have been developed and evaluated using a number popular and publicly available sensor data sets. Experimental results for FPool demonstrate improved classification and prediction performance when compared to alternative methods. Moreover, FPool shows an important reduction in the training time over the basic DiffPool framework.

Keywords

Knowledge graphs; hierarchical pooling; graph classification; graph neural networks; FPool; large graph sensor datasets

Subject

MATHEMATICS & COMPUTER SCIENCE, Algebra & Number Theory

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