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.
Ranked #1 on Node Classification on YouTube
In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research.
We study the problem of learning representations of entities and relations in knowledge graphs for predicting missing links.
Ranked #4 on Link Prediction on WN18
This work presents Contextualized Knowledge Graph Embedding (CoKE), a novel paradigm that takes into account such contextual nature, and learns dynamic, flexible, and fully contextualized entity and relation embeddings.
Python library for knowledge graph embedding and representation learning.
Recently, knowledge graph embeddings (KGEs) received significant attention, and several software libraries have been developed for training and evaluating KGEs.
Multi-relational graphs are a more general and prevalent form of graphs where each edge has a label and direction associated with it.
Ranked #13 on Link Prediction on FB15k-237