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

Most implemented papers

Graph Attention Networks

PetarV-/GAT ICLR 2018

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

tkipf/relational-gcn 17 Mar 2017

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

node2vec: Scalable Feature Learning for Networks

dmlc/dgl 3 Jul 2016

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

tkipf/gae 21 Nov 2016

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

xiangwang1223/neural_graph_collaborative_filtering 20 May 2019

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

riannevdberg/gc-mc 7 Jun 2017

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

Inductive Representation Learning on Large Graphs

williamleif/GraphSAGE NeurIPS 2017

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

dmlc/dgl NeurIPS 2018

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

graphdeeplearning/benchmarking-gnns 2 Mar 2020

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

RexYing/gnn-model-explainer NeurIPS 2019

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