Pattern recognition in the graphs domain gained a lot of attention in the last two decades, since graphs are able to describe relationships (edges) between atomic entities (nodes) which can further be equipped with attributes encoding meaningful information. In this work, we investigate a novel graph embedding procedure based on the Granular Computing paradigm. Conversely to recently-developed techniques, we propose a stratified procedure for extracting suitable information granules (namely, frequent and/or meaningful subgraphs) in a class-aware fashion; that is, each class for the classification problem at hand is represented by the set of its own pivotal information granules. Computational results on several open-access datasets show performance improvements when considering also the ground-truth class labels in the information granulation procedure. Furthermore, since the granulation procedure is based on random walks, it is also very appealing in Big Data scenarios.

Towards a Class-Aware Information Granulation for Graph Embedding and Classification / Baldini, Luca; Martino, Alessio; Rizzi, Antonello. - 922:(2021), pp. 263-290. [10.1007/978-3-030-70594-7_11]

Towards a Class-Aware Information Granulation for Graph Embedding and Classification

MARTINO A;
2021

Abstract

Pattern recognition in the graphs domain gained a lot of attention in the last two decades, since graphs are able to describe relationships (edges) between atomic entities (nodes) which can further be equipped with attributes encoding meaningful information. In this work, we investigate a novel graph embedding procedure based on the Granular Computing paradigm. Conversely to recently-developed techniques, we propose a stratified procedure for extracting suitable information granules (namely, frequent and/or meaningful subgraphs) in a class-aware fashion; that is, each class for the classification problem at hand is represented by the set of its own pivotal information granules. Computational results on several open-access datasets show performance improvements when considering also the ground-truth class labels in the information granulation procedure. Furthermore, since the granulation procedure is based on random walks, it is also very appealing in Big Data scenarios.
2021
978-3-030-70594-7
Towards a Class-Aware Information Granulation for Graph Embedding and Classification / Baldini, Luca; Martino, Alessio; Rizzi, Antonello. - 922:(2021), pp. 263-290. [10.1007/978-3-030-70594-7_11]
File in questo prodotto:
File Dimensione Formato  
bok-978-3-030-70594-7 copy.pdf

Solo gestori archivio

Tipologia: Versione dell'editore
Licenza: DRM (Digital rights management) non definiti
Dimensione 586.83 kB
Formato Adobe PDF
586.83 kB Adobe PDF   Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11385/214499
Citazioni
  • Scopus 14
  • ???jsp.display-item.citation.isi??? 2
  • OpenAlex ND
social impact