DyVGRNN: DYnamic mixture Variational Graph Recurrent Neural Networks.
Neural Netw
; 165: 596-610, 2023 Aug.
Article
em En
| MEDLINE
| ID: mdl-37364470
ABSTRACT
Although graph representation learning has been studied extensively in static graph settings, dynamic graphs are less investigated in this context. This paper proposes a novel integrated variational framework called DYnamic mixture Variational Graph Recurrent Neural Networks (DyVGRNN), which consists of extra latent random variables in structural and temporal modelling. Our proposed framework comprises an integration of Variational Graph Auto-Encoder (VGAE) and Graph Recurrent Neural Network (GRNN) by exploiting a novel attention mechanism. The Gaussian Mixture Model (GMM) and the VGAE framework are combined in DyVGRNN to model the multimodal nature of data, which enhances performance. To consider the significance of time steps, our proposed method incorporates an attention-based module. The experimental results demonstrate that our method greatly outperforms state-of-the-art dynamic graph representation learning methods in terms of link prediction and clustering.2.
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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Redes Neurais de Computação
/
Aprendizagem
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article