[en] Graph related tasks, such as graph classification and clustering, have been substantially improved with the advent of graph neural networks (GNNs). However, existing graph embedding models focus on homogeneous graphs that ignore the heterogeneity of the graphs. Therefore, using homogeneous graph embedding models on heterogeneous graphs discards the rich semantics of graphs and achieves average performance, especially by utilizing unlabeled information. However, limited work has been done on whole heterogeneous graph embedding as a supervised task. In light of this, we investigate unsupervised distributed representations learning on heterogeneous graphs and propose a novel model named G-HIN2Vec, Graph-Level Heterogeneous Information Network to Vector. Inspired by recent advances of unsupervised learning in natural language processing, G-HIN2Vec utilizes negative sampling technique as an unlabeled approach and learns graph embedding matrix from different predefined meta-paths. We conduct a variety of experiments on three main graph downstream applications on different socio-demographic cardholder features, graph regression, graph clustering, and graph classification, such as gender classification, age, and income prediction, which shows superior performance of our proposed GNN model on real-world financial credit card data.
Centre de recherche :
- Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SEDAN - Service and Data Management in Distributed Systems
Disciplines :
Sciences informatiques
Auteur, co-auteur :
DAMOUN, Farouk ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SEDAN
Seba, Hamida; Université Claude Bernard - Lyon 1 - UCLB
HILGER, Jean ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SnT Finnovation Hub
STATE, Radu ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SEDAN
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
G-HIN2Vec: Distributed Heterogeneous Graph Representations for Cardholder Transactions
Date de publication/diffusion :
27 mars 2023
Nom de la manifestation :
The 38th ACM/SIGAPP Symposium On Applied Computing
Organisateur de la manifestation :
Association for Computing Machinery
Lieu de la manifestation :
Tallinn, Estonie
Date de la manifestation :
March 27 - March 31, 2023
Sur invitation :
Oui
Manifestation à portée :
International
Titre de l'ouvrage principal :
Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing
Maison d'édition :
Association for Computing Machinery, New York, Inconnu/non spécifié
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