With this framework, encoder generates graph embedding at node level, and decoder use the em-bedding to perform prediction/classification. Practitioners could assemble different encoder and decoder combinations to best fit their machine learning task. Moreover, the decoder can be modified to perform all dynamic graph learning tasks

Mar 20, 2022 · Under this framework, this survey categories and reviews different learnable encoder-decoder architectures for supervised dynamic graph learning. We believe that this survey could supply useful guidelines to researchers and engineers in finding suitable graph structures for their dynamic learning tasks.

Our method adopts an encoder-decoder architecture. The STRE module is pivotal for encoding the spatial-temporal representation, while the output and prediction module facilitate decoding and predicting future traffic flows.

AGE adopts the encoder-decoder structure, where the encoder tries to learn the representation of conditioned graphs using a self-attention mechanism, and the decoder tries to generate the representation of the target graphs using the correlation …

Mar 22, 2025 · REFD addresses this by jointly modeling the evolution of both entities and relations over time through a recurrent encoder and a fusion decoder. The recurrent encoder captures temporal dependencies across different scales, integrating dynamic entity and relation features into a shared representation.

In this paper, we propose an unsuper-vised representation learning architecture for dy-namic graphs, designed to learn both the topologi-cal and temporal features of the graphs that evolve over time. The approach consists of a sequence-to-sequence encoder-decoder model embedded with gated graph neural networks (GGNNs).

With this framework, encoder generates graph embedding at node level, and decoder use the embedding to perform prediction/classification. Practitioners could assemble different encoder and decoder combinations to best fit their machine learning task.

Firstly, an encoder model is employed to map each node in the graph to a compact, low-dimensional vector, known as embedding. These embeddings are then passed as input to a decoder model that aims to reconstruct the local neighborhood information for each …

By integrating MDPool with the graph neural network module, we have developed an encoder-decoder framework for graph classification, called EDMDPool. Experimental results show that EDMDPool achieves accuracy improvements of 3.06%, 0.98%, 1.24%, and 1.80% over existing methods on four datasets.

AGE adopts the encoder-decoder structure, where the encoder tries to learn the representation of conditioned graphs using a self-attention mechanism, and the decoder tries to generate the representation of the target graphs using the correlation …