Complexity vs. Performance in Granular Embedding Spaces for Graph Classification

Luca Baldini, Alessio Martino, Antonello Rizzi

2020

Abstract

The most distinctive trait in structural pattern recognition in graph domain is the ability to deal with the organization and relations between the constituent entities of the pattern. Even if this can be convenient and/or necessary in many contexts, most of the state-of the art classification techniques can not be deployed directly in the graph domain without first embedding graph patterns towards a metric space. Granular Computing is a powerful information processing paradigm that can be employed in order to drive the synthesis of automatic embedding spaces from structured domains. In this paper we investigate several classification techniques starting from Granular Computing-based embedding procedures and provide a thorough overview in terms of model complexity, embedding space complexity and performances on several open-access datasets for graph classification. We witness that certain classification techniques perform poorly both from the point of view of complexity and learning performances as the case of non-linear SVM, suggesting that high dimensionality of the synthesized embedding space can negatively affect the effectiveness of these approaches. On the other hand, linear support vector machines, neuro-fuzzy networks and nearest neighbour classifiers have comparable performances in terms of accuracy, with second being the most competitive in terms of structural complexity and the latter being the most competitive in terms of embedding space dimensionality.

Paper Citation

in Harvard Style

Baldini L., Martino A. and Rizzi A. (2020). Complexity vs. Performance in Granular Embedding Spaces for Graph Classification. In Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: NCTA; ISBN 978-989-758-475-6, SciTePress, pages 338-349. DOI: 10.5220/0010109503380349

in Bibtex Style

@conference{ncta20,
author={Luca Baldini and Alessio Martino and Antonello Rizzi},
title={Complexity vs. Performance in Granular Embedding Spaces for Graph Classification},
booktitle={Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: NCTA},
year={2020},
pages={338-349},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010109503380349},
isbn={978-989-758-475-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: NCTA
TI - Complexity vs. Performance in Granular Embedding Spaces for Graph Classification
SN - 978-989-758-475-6
AU - Baldini L.
AU - Martino A.
AU - Rizzi A.
PY - 2020
SP - 338
EP - 349
DO - 10.5220/0010109503380349
PB - SciTePress