Link prediction is a task to estimate the probability of links between nodes in a graph.
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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.
#10 best model for Node Classification on PubMed with Public Split: fixed 20 nodes per class
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.
#3 best model for Document Classification on Cora
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.
In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases.
#6 best model for Link Prediction on WN18
We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces.
This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction.
#5 best model for Node Classification on Wikipedia