This is the implementation of paper "Variational Graph Auto-Encoders", which is published in NIPS 2016 Workshop. Thomas N. Kipf, Max Welling, Variational Graph Auto-Encoders, In NIPS Workshop on ...

Auto-encoders have emerged as a successful framework for unsupervised learning. However, conventional auto-encoders are incapable of utilizing explicit relations in structured data. To take advantage ...

graph link-prediction subgraph self-supervised-learning node-embedding graph-auto-encoder contrastive-learning Updated on May 30, 2023 Python ...

This is followed by an introduction of our encoder model GI-KBGAT, an improved Graph Attention Network for KG, which considers gate mechanism on multi-head attention and interaction between entities ...

Graph based clustering plays an important role in clustering area. Recent studies about graph convolution neural networks have achieved impressive success on graph type data. However, in traditional ...

Auto-encoders have emerged as a successful framework for unsupervised learning. However, conventional auto-encoders are incapable of utilizing explicit relations in structured data. To take advantage ...