Link Prediction
Link prediction is a task to estimate the probability of links between nodes in a graph.
( Image credit: Inductive Representation Learning on Large Graphs )
Leaderboards
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Greatest papers with code
PyTorch-BigGraph: A Large-scale Graph Embedding System
• facebookresearch/PyTorch-BigGraph
• 
Graph embedding methods produce unsupervised node features from graphs that can then be used for a variety of machine learning tasks.
Embedding Entities and Relations for Learning and Inference in Knowledge Bases
• 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
Inductive Representation Learning on Large Graphs
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.
#5 best model for
Node Classification
on Cora Full-supervised
Graph Attention Networks
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.
#2 best model for
Skeleton Based Action Recognition
on J-HMBD Early Action
DOCUMENT CLASSIFICATION GRAPH EMBEDDING GRAPH REGRESSION LINK PREDICTION NODE CLASSIFICATION SKELETON BASED ACTION RECOGNITION
Knowledge Graph Completion via Complex Tensor Factorization
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
KNOWLEDGE GRAPH COMPLETION LINK PREDICTION RELATIONAL REASONING
Complex Embeddings for Simple Link Prediction
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
Holographic Embeddings of Knowledge Graphs
Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs.
Translating Embeddings for Modeling Multi-relational Data
NeurIPS 2013
• Accenture/AmpliGraph
•
We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces.
#6 best model for
Link Prediction
on WN18RR
Graph Convolutional Matrix Completion
• tkipf/gae
•
We consider matrix completion for recommender systems from the point of view of link prediction on graphs.
Modeling Relational Data with Graph Convolutional Networks
• tkipf/gae
•
We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.
SOTA for Node Classification on AIFB
GRAPH CLASSIFICATION INFORMATION RETRIEVAL KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPHS LINK PREDICTION NODE CLASSIFICATION