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In this paper, we propose GraphVite, a high-performance CPU-GPU hybrid system for training node embeddings, by co-optimizing the algorithm and the system.
SOTA for Node Classification on YouTube
This framework is independent of the concrete form of generator and discriminator, and therefore can utilize a wide variety of knowledge graph embedding models as its building blocks.
A visual-relational knowledge graph (KG) is a multi-relational graph whose entities are associated with images.
Collaborative filtering often suffers from sparsity and cold start problems in real recommendation scenarios, therefore, researchers and engineers usually use side information to address the issues and improve the performance of recommender systems.
Python library for knowledge graph embedding and representation learning.
Knowledge Graph (KG) embedding has emerged as an active area of research resulting in the development of several KG embedding methods.
Most conventional knowledge embedding methods encode both entities (concepts and instances) and relations as vectors in a low dimensional semantic space equally, ignoring the difference between concepts and instances.