Stochastic Information Granules Extraction for Graph Embedding and Classification

Luca Baldini, Alessio Martino, Antonello Rizzi

2019

Abstract

Graphs are data structures able to efficiently describe real-world systems and, as such, have been extensively used in recent years by many branches of science, including machine learning engineering. However, the design of efficient graph-based pattern recognition systems is bottlenecked by the intrinsic problem of how to properly match two graphs. In this paper, we investigate a granular computing approach for the design of a general purpose graph-based classification system. The overall framework relies on the extraction of meaningful pivotal substructures on the top of which an embedding space can be build and in which the classification can be performed without limitations. Due to its importance, we address whether information can be preserved by performing stochastic extraction on the training data instead of performing an exhaustive extraction procedure which is likely to be unfeasible for large datasets. Tests on benchmark datasets show that stochastic extraction can lead to a meaningful set of pivotal substructures with a much lower memory footprint and overall computational burden, making the proposed strategies suitable also for dealing with big datasets.

Paper Citation

in Harvard Style

Baldini L., Martino A. and Rizzi A. (2019). Stochastic Information Granules Extraction for Graph Embedding and Classification. In Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - Volume 1: NCTA; ISBN 978-989-758-384-1, SciTePress, pages 391-402. DOI: 10.5220/0008149403910402

in Bibtex Style

@conference{ncta19,
author={Luca Baldini and Alessio Martino and Antonello Rizzi},
title={Stochastic Information Granules Extraction for Graph Embedding and Classification},
booktitle={Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - Volume 1: NCTA},
year={2019},
pages={391-402},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008149403910402},
isbn={978-989-758-384-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - Volume 1: NCTA
TI - Stochastic Information Granules Extraction for Graph Embedding and Classification
SN - 978-989-758-384-1
AU - Baldini L.
AU - Martino A.
AU - Rizzi A.
PY - 2019
SP - 391
EP - 402
DO - 10.5220/0008149403910402
PB - SciTePress