About
Link prediction is a task to estimate the probability of links between nodes in a graph.
( Image credit: Inductive Representation Learning on Large Graphs )
Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
Subtasks
Datasets
Greatest papers with code
Neural Collaborative Filtering vs. Matrix Factorization Revisited
• google-research/google-research
• 
This approach is often referred to as neural collaborative filtering (NCF).
Ranked #6 on
Link Prediction
on Yelp
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
KDD 2019
• google-research/google-research
•
Furthermore, Cluster-GCN allows us to train much deeper GCN without much time and memory overhead, which leads to improved prediction accuracy---using a 5-layer Cluster-GCN, we achieve state-of-the-art test F1 score 99. 36 on the PPI dataset, while the previous best result was 98. 71 by [16].
Ranked #1 on
Node Classification
on Pubmed
(F1 metric)
Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts
• google-research/google-research
•
Recent interest in graph embedding methods has focused on learning a single representation for each node in the graph.
Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning
• dmlc/dgl
• 
Motivated by this observation, we propose a graph representation learning method called Graph InfoClust (GIC), that seeks to additionally capture cluster-level information content.
Ranked #1 on
Link Prediction
on Citeseer
GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION NODE CLUSTERING
Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs
• dmlc/dgl
•
The task becomes more challenging on temporal knowledge graphs, where each fact is associated with a timestamp.
Graph Attention Networks
ICLR 2018
• aymericdamien/TopDeepLearning
•
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.
Ranked #2 on
Node Classification
on Wiki-Vote
DOCUMENT CLASSIFICATION GRAPH CLASSIFICATION GRAPH EMBEDDING GRAPH REGRESSION LINK PREDICTION NODE CLASSIFICATION SKELETON BASED ACTION RECOGNITION
Neural Factorization Machines for Sparse Predictive Analytics
However, FM models feature interactions in a linear way, which can be insufficient for capturing the non-linear and complex inherent structure of real-world data.
Ranked #2 on
Link Prediction
on MovieLens 25M
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.
Ranked #1 on
Link Prediction
on YouTube
(Macro F1 metric)
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.
Ranked #15 on
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.
Ranked #2 on
Node Classification
on Flickr
GRAPH CLASSIFICATION GRAPH REGRESSION LINK PREDICTION NODE CLASSIFICATION REPRESENTATION LEARNING