Graphs
 
Edit

Link Prediction

807 papers with code • 78 benchmarks • 63 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

Datasets

Subtasks

Most implemented papers

Most implemented Social Latest No code

Link Prediction Based on Graph Neural Networks

muhanzhang/SEAL • • NeurIPS 2018

The theory unifies a wide range of heuristics in a single framework, and proves that all these heuristics can be well approximated from local subgraphs.

10
Paper
Code

RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space

DeepGraphLearning/KnowledgeGraphEmbedding • • ICLR 2019

We study the problem of learning representations of entities and relations in knowledge graphs for predicting missing links.

10
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

Embedding Entities and Relations for Learning and Inference in Knowledge Bases

pyg-team/pytorch_geometric • • 20 Dec 2014

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

9
Paper
Code

Hyperspherical Variational Auto-Encoders

nicola-decao/s-vae-pytorch • • 3 Apr 2018

But although the default choice of a Gaussian distribution for both the prior and posterior represents a mathematically convenient distribution often leading to competitive results, we show that this parameterization fails to model data with a latent hyperspherical structure.

9
Paper
Code

Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction

MIRALab-USTC/KGE-HAKE • • 21 Nov 2019

HAKE is inspired by the fact that concentric circles in the polar coordinate system can naturally reflect the hierarchy.

9
Paper
Code

Translating Embeddings for Modeling Multi-relational Data

pyg-team/pytorch_geometric • • NeurIPS 2013

We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces.

8
Paper
Code

LINE: Large-scale Information Network Embedding

tangjianpku/LINE • 12 Mar 2015

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.

8
Paper
Code

Complex Embeddings for Simple Link Prediction

ttrouill/complex • 20 Jun 2016

In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases.

8
Paper
Code

Convolutional 2D Knowledge Graph Embeddings

TimDettmers/ConvE • • 5 Jul 2017

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.

8
Paper
Code
Contact us on: hello@paperswithcode.com . Papers With Code is a free resource with all data licensed under CC-BY-SA.
Terms Data policy Cookies policy