125 papers with code ·
Graphs

Link prediction is a task to estimate the probability of links between nodes in a graph.

facebookresearch/PyTorch-BigGraph •

•Graph embedding methods produce unsupervised node features from graphs that can then be used for a variety of machine learning tasks.

facebookresearch/PyTorch-BigGraph •

•We consider learning representations of entities and relations in KBs using the neural-embedding approach.

#7 best model for Link Prediction on WN18

NeurIPS 2017 • williamleif/GraphSAGE •

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

ICLR 2018 • PetarV-/GAT •

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

DOCUMENT CLASSIFICATION GRAPH EMBEDDING LINK PREDICTION NODE CLASSIFICATION

ICML 2018 • Accenture/AmpliGraph •

The problem of Knowledge Base Completion can be framed as a 3rd-order binary tensor completion problem.

SOTA for Link Prediction on FB15k-237

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.

KNOWLEDGE GRAPH COMPLETION LINK PREDICTION RELATIONAL REASONING

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

Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs.

NeurIPS 2013 • Accenture/AmpliGraph •

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

GRAPH EMBEDDING LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION