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Found 6 papers, 1 papers with code
Variational Graph Auto-Encoder Based Inductive Learning Method for Semi-Supervised Classification
Graph representation learning is a fundamental research issue in various domains of applications, of which the inductive learning problem is particularly challenging as it requires models to generalize to unseen graph structures during inference.
YAYI 2: Multilingual Open-Source Large Language Models
As the latest advancements in natural language processing, large language models (LLMs) have achieved human-level language understanding and generation abilities in many real-world tasks, and even have been regarded as a potential path to the artificial general intelligence.
Isomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning
To overcome the limitations of existing unsupervised methods, in this paper, we propose the Isomorphic-Consistent VGAE (IsoC-VGAE) for multi-level task-agnostic graph representation learning.
Spiking Variational Graph Auto-Encoders for Efficient Graph Representation Learning
Graph representation learning is a fundamental research issue and benefits a wide range of applications on graph-structured data.
A Deep Latent Space Model for Directed Graph Representation Learning
We propose a Deep Latent Space Model (DLSM) for directed graphs to incorporate the traditional latent space random graph model into deep learning frameworks via a hierarchical variational auto-encoder architecture.
A Deep Latent Space Model for Graph Representation Learning
Our proposed model consists of a graph convolutional network (GCN) encoder and a stochastic decoder, which are layer-wise connected by a hierarchical variational auto-encoder architecture.
