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Link Prediction

86 papers with code · Graphs

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

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Greatest papers with code

PyTorch-BigGraph: A Large-scale Graph Embedding System

28 Mar 2019facebookresearch/PyTorch-BigGraph

Graph embedding methods produce unsupervised node features from graphs that can then be used for a variety of machine learning tasks.

GRAPH EMBEDDING LINK PREDICTION

Embedding Entities and Relations for Learning and Inference in Knowledge Bases

20 Dec 2014facebookresearch/PyTorch-BigGraph

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

LINK PREDICTION

Inductive Representation Learning on Large Graphs

NeurIPS 2017 williamleif/GraphSAGE

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.

LINK PREDICTION NODE CLASSIFICATION REPRESENTATION LEARNING

LINE: Large-scale Information Network Embedding

12 Mar 2015tangjianpku/LINE

This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction.

GRAPH EMBEDDING LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION

Graph Convolutional Matrix Completion

7 Jun 2017tkipf/gae

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

COLLABORATIVE FILTERING LINK PREDICTION MATRIX COMPLETION

Modeling Relational Data with Graph Convolutional Networks

17 Mar 2017tkipf/gae

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

KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPHS LINK PREDICTION

Variational Graph Auto-Encoders

21 Nov 2016tkipf/gae

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

LINK PREDICTION

Graph Attention Networks

ICLR 2018 PetarV-/GAT

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 EMBEDDING LINK PREDICTION NODE CLASSIFICATION

Higher-Order Factorization Machines

NeurIPS 2016 geffy/tffm

Factorization machines (FMs) are a supervised learning approach that can use second-order feature combinations even when the data is very high-dimensional.

LINK PREDICTION

Convolutional 2D Knowledge Graph Embeddings

5 Jul 2017TimDettmers/ConvE

In this work, we introduce ConvE, a multi-layer convolutional network model for link prediction, and report state-of-the-art results for several established datasets.

KNOWLEDGE GRAPH EMBEDDINGS KNOWLEDGE GRAPHS LINK PREDICTION