Experimental Investigation of Neural and Weisfeiler-Lehman-Kernel Graph Representations for Downstream SVM-Based Classification

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F21%3A00546251" target="_blank" >RIV/67985807:_____/21:00546251 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21240/21:00382640 RIV/68407700:21340/21:00382640

Result on the web

<a href="https://ics.upjs.sk/~antoni/ceur-ws.org/Vol-0000/paper50.pdf" target="_blank" >https://ics.upjs.sk/~antoni/ceur-ws.org/Vol-0000/paper50.pdf</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Experimental Investigation of Neural and Weisfeiler-Lehman-Kernel Graph Representations for Downstream SVM-Based Classification

Original language description

Graphs are one of the most ubiquitous kinds of data. However, data analysis methods have been developed primarily for numerical data, and to make use of them, graphs need to be represented as elements of some Euclidean space. An increasingly popular way of representing them in this way are graph neural networks´(GNNs). Because data analysis applications typically require identical results for isomorphic graphs, the representations learned by GNNs also need to be invariant with respect to graph isomorphism. That motivated recent research into the possibilities of recognizing nonisomorphic pairs of graphs by GNNs, primarily based on the Weisfeiler-Lehman (WL) isomorphism test. This paper reports the results of a first experimental comparison of four variants of two important GNNs based on the WL test from the point of view of graph representation for downstream classification by means of a support vector machins (SVM). Those methods are compared not only with each other, but also with a recent generalization of the WL subtree kernel. For all GNN variants, two different representations are included in the comparison. The comparison revealed that the four considered representations of the same kind of GNN never significantly differ. On the other hand, there was always a statistically significant difference between representations originating from different kinds of GNNs, as well as between any representation originating from any of the considered GNNs and the representation originating from the generalized WL kernel.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA18-18080S" target="_blank" >GA18-18080S: Fusion-Based Knowledge Discovery in Human Activity Data</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the 21st Conference Information Technologies – Applications and Theory (ITAT 2021)

ISBN

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ISSN

1613-0073

e-ISSN

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Number of pages

10

Pages from-to

130-139

Publisher name

Technical University & CreateSpace Independent Publishing

Place of publication

Aachen

Event location

Heľpa

Event date

Sep 24, 2021

Type of event by nationality

EUR - Evropská akce

UT code for WoS article

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