# Link Prediction

603 papers with code • 73 benchmarks • 56 datasets

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

( Image credit: Inductive Representation Learning on Large Graphs )

## Libraries

Use these libraries to find Link Prediction models and implementations## Subtasks

## Most implemented papers

# Graph Attention Networks

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.

# Modeling Relational Data with Graph Convolutional Networks

We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.

# node2vec: Scalable Feature Learning for Networks

Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.

# Variational Graph Auto-Encoders

We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE).

# Neural Graph Collaborative Filtering

Further analysis verifies the importance of embedding propagation for learning better user and item representations, justifying the rationality and effectiveness of NGCF.

# Graph Convolutional Matrix Completion

We consider matrix completion for recommender systems from the point of view of link prediction on graphs.

# Inductive Representation Learning on Large Graphs

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.

# Hierarchical Graph Representation Learning with Differentiable Pooling

Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction.

# Benchmarking Graph Neural Networks

In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs.

# GNNExplainer: Generating Explanations for Graph Neural Networks

We formulate GNNExplainer as an optimization task that maximizes the mutual information between a GNN's prediction and distribution of possible subgraph structures.