Link prediction is a task to estimate the probability of links between nodes in a graph.
( Image credit: Inductive Representation Learning on Large Graphs )
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We consider learning representations of entities and relations in KBs using the neural-embedding approach.
#12 best model for Link Prediction on WN18
Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions.
#5 best model for Node Classification on Cora Full-supervised
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.
#2 best model for Skeleton Based Action Recognition on J-HMBD Early Action
In statistical relational learning, knowledge graph completion deals with automatically understanding the structure of large knowledge graphs---labeled directed graphs---and predicting missing relationships---labeled edges.
SOTA for Knowledge Graphs on FB15k
We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces.
#12 best model for Link Prediction on WN18RR
We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.
SOTA for Node Classification on AIFB