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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.
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.
Recently, knowledge graph embeddings (KGEs) received significant attention, and several software libraries have been developed for training and evaluating KGEs.
Ranked #1 on Link Prediction on WN18 (training time (s) metric)
The heterogeneity in recently published knowledge graph embedding models' implementations, training, and evaluation has made fair and thorough comparisons difficult.
A vast number of KGE techniques for multi-relational link prediction have been proposed in the recent literature, often with state-of-the-art performance.
Multi-relational graphs are a more general and prevalent form of graphs where each edge has a label and direction associated with it.
Ranked #12 on Link Prediction on FB15k-237