In this paper, a method of knowledge reasoning and completion based on neural networks on the knowledge graph is designed for robots to simulate the reaction and learning process of human brains. Our ...

In particular, we consider an underparametrized graph-aware NN encoder that maps the input graph signal to a latent space, followed by an underparametrized graph-aware NN decoder which maps the latent ...

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 ...

each line is a json object whose keys are "seq", "g_ids", "g_id_features", "g_adj": "seq" is a text which is supposed to be the output of the decoder "g_ids" is a ...

By combining generative model for graph embedding and graph based clustering, a graph auto-encoder with a novel decoder is developed and it performs well in weighted graph used scenarios. Extensive ...

This repository contains code for 2 Keras layers which comprise a deep learning based encoder and a decoder for fixed interval shapelets along a multi-channel time series graph. I'm not sure how ...

We propose GrainGNN, a surrogate model for the evolution ... and altering graph algorithms with two neural networks, a classifier (for topological changes) and a regressor (for interface motion). Both ...