[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.
Research center :
- Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SEDAN - Service and Data Management in Distributed Systems
Disciplines :
Computer science
Author, co-author :
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
External co-authors :
yes
Language :
English
Title :
G-HIN2Vec: Distributed Heterogeneous Graph Representations for Cardholder Transactions
Publication date :
27 March 2023
Event name :
The 38th ACM/SIGAPP Symposium On Applied Computing
Event organizer :
Association for Computing Machinery
Event place :
Tallinn, Estonia
Event date :
March 27 - March 31, 2023
By request :
Yes
Audience :
International
Main work title :
Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing
Publisher :
Association for Computing Machinery, New York, Unknown/unspecified
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