SGVAE: Sequential Graph Variational Autoencoder
Generative models of graphs are well-known, but many existing models are limited in scalability and expressivity. We present a novel sequential graphical variational autoencoder operating directly on graphical representations of data. In our model, the encoding and decoding of a graph as is framed as a sequential deconstruction and construction process, respectively, enabling the the learning of a latent space. Experiments on a cycle dataset show promise, but highlight the need for a relaxation of the distribution over node permutations.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A Variational-Sequential Graph Autoencoder for Neural Architecture Performance Prediction
In computer vision research, the process of automating architecture engineering, Neural Architecture Search (NAS), has gained substantial interest. In the past, NAS was hardly accessible to researchers without access to …
Neural Architecture SearchPredictionConstrained Graph Variational Autoencoders for Molecule Design
Graphs are ubiquitous data structures for representing interactions between entities. With an emphasis on the use of graphs to represent chemical molecules, we explore the task of learning to generate graphs that conform…
DecoderContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation
Aiming at exploiting the rich information in user behaviour sequences, sequential recommendation has been widely adopted in real-world recommender systems. However, current methods suffer from the following issues: 1) sp…
Contrastive LearningRecommendation SystemsSequential RecommendationContrastively Disentangled Sequential Variational Autoencoder
Self-supervised disentangled representation learning is a critical task in sequence modeling. The learnt representations contribute to better model interpretability as well as the data generation, and improve the sample …
Representation LearningAdversarial and Contrastive Variational Autoencoder for Sequential Recommendation
Sequential recommendation as an emerging topic has attracted increasing attention due to its important practical significance. Models based on deep learning and attention mechanism have achieved good performance in seque…
Collaborative FilteringSequential Recommendation