Link Prediction

460 papers with code • 69 benchmarks • 45 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 )

Greatest papers with code

Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

google-research/google-research KDD 2019

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].

Graph Clustering Link Prediction +1

Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts

google-research/google-research 6 May 2019

Recent interest in graph embedding methods has focused on learning a single representation for each node in the graph.

Graph Embedding Link Prediction

How Attentive are Graph Attention Networks?

rusty1s/pytorch_geometric 30 May 2021

We formally define this restricted kind of attention as static attention and distinguish it from a strictly more expressive dynamic attention.

Graph Attention Graph Property Prediction +3

Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs

dmlc/dgl 11 Apr 2019

The task becomes more challenging on temporal knowledge graphs, where each fact is associated with a timestamp.

Knowledge Graphs Link Prediction

CoKE: Contextualized Knowledge Graph Embedding

PaddlePaddle/models 6 Nov 2019

This work presents Contextualized Knowledge Graph Embedding (CoKE), a novel paradigm that takes into account such contextual nature, and learns dynamic, flexible, and fully contextualized entity and relation embeddings.

Knowledge Graph Embedding Link Prediction

Neural Factorization Machines for Sparse Predictive Analytics

shenweichen/DeepCTR 16 Aug 2017

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.

Link Prediction

Graph Attention Networks

labmlai/annotated_deep_learning_paper_implementations 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.

Document Classification Graph Attention +8

PyTorch-BigGraph: A Large-scale Graph Embedding System

facebookresearch/PyTorch-BigGraph 28 Mar 2019

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)

Graph Embedding graph partitioning +1

Embedding Entities and Relations for Learning and Inference in Knowledge Bases

facebookresearch/PyTorch-BigGraph 20 Dec 2014

We consider learning representations of entities and relations in KBs using the neural-embedding approach.

Link Prediction