# Link Prediction

719 papers with code • 75 benchmarks • 58 datasets

**Link Prediction** is a task in graph and network analysis where the goal is to predict missing or future connections between nodes in a network. Given a partially observed network, the goal of link prediction is to infer which links are most likely to be added or missing based on the observed connections and the structure of the network.

( Image credit: Inductive Representation Learning on Large Graphs )

## Libraries

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

## Most implemented papers

# Attention Is All You Need

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.

# 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.

# 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).

# 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.

# 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.

# 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.

# Graph Convolutional Matrix Completion

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

# 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.