Graph Neural Networks (GNNs) are specialized machine learning models designed for analyzing and making predictions based on graph-structured data. In this paper, we introduce a GNN-based approach for the analysis of heterogeneous event graphs. By heterogeneous event graph, we refer to a temporal graph where multiple types of nodes representing events as well as multiple type of edges among events are defined. A distinguishing aspect of the proposed approach lies in the specification of the subgraph of nodes for each event to classify, and in the use of embedding techniques for representing all the relevant features/attributes of the subgraph. The proposed approach is compatible with different GNN models; the use of PSCN, namely a CNN for subgraph classification, is discussed in the paper under two different settings. Preliminary results on two different case-studies, as well as a real application in the field of historical data classification are also discussed.

Using Graph Neural Networks for Heterogeneous Event Classification / V. Bellandi, S. Montanelli, D. Shlyk, S. Siccardi (CEUR WORKSHOP PROCEEDINGS). - In: CEUR Workshop Proceedings / [a cura di] M. Atzori, P. Ciaccia, M. Ceci, F. Mandreoli, D. Malerba, M. Sanguinetti, A. Pellicani, F. Motta. - [s.l] : CEUR, 2024. - pp. 247-259 (( Intervento presentato al 32. convegno SEBD Italian Symposium on Advanced Database Systems : June 23rd to 26th tenutosi a Villasimius nel 2024.

Using Graph Neural Networks for Heterogeneous Event Classification

V. Bellandi
Primo
;
S. Montanelli
Secondo
;
D. Shlyk
Penultimo
;
S. Siccardi
Ultimo
2024

Abstract

Graph Neural Networks (GNNs) are specialized machine learning models designed for analyzing and making predictions based on graph-structured data. In this paper, we introduce a GNN-based approach for the analysis of heterogeneous event graphs. By heterogeneous event graph, we refer to a temporal graph where multiple types of nodes representing events as well as multiple type of edges among events are defined. A distinguishing aspect of the proposed approach lies in the specification of the subgraph of nodes for each event to classify, and in the use of embedding techniques for representing all the relevant features/attributes of the subgraph. The proposed approach is compatible with different GNN models; the use of PSCN, namely a CNN for subgraph classification, is discussed in the paper under two different settings. Preliminary results on two different case-studies, as well as a real application in the field of historical data classification are also discussed.
GNN; Historical Data Analysis; Multi-layer Event Classification;
Settore INFO-01/A - Informatica
   MUSA - Multilayered Urban Sustainability Actiona
   MUSA
   MINISTERO DELL'UNIVERSITA' E DELLA RICERCA

   SEcurity and RIghts in the CyberSpace (SERICS)
   SERICS
   MINISTERO DELL'UNIVERSITA' E DELLA RICERCA
   codice identificativo PE00000014

   Piano di Sostegno alla Ricerca 2015-2017 - Linea 2 "Dotazione annuale per attività istituzionali" (anno 2021)
   UNIVERSITA' DEGLI STUDI DI MILANO
2024
CEUR
https://ceur-ws.org/Vol-3741/
Book Part (author)
File in questo prodotto:
File Dimensione Formato  
paper36.pdf

accesso aperto

Tipologia: Publisher's version/PDF
Dimensione 1.2 MB
Formato Adobe PDF
1.2 MB 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/2434/1122501
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact