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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 )

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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.

77
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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.

22
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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.

17
Paper
Code

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).

17
Paper
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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.

16
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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.

15
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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.

13
Paper
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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.

13
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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.

13
Paper
Code

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.

10
Paper
Code
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