Link Prediction
811 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 implementationsSubtasks
Most implemented papers
Link Prediction Based on Graph Neural Networks
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.
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
We study the problem of learning representations of entities and relations in knowledge graphs for predicting missing links.
GNNExplainer: Generating Explanations for Graph Neural Networks
We formulate GNNExplainer as an optimization task that maximizes the mutual information between a GNN's prediction and distribution of possible subgraph structures.
Embedding Entities and Relations for Learning and Inference in Knowledge Bases
We consider learning representations of entities and relations in KBs using the neural-embedding approach.
Hyperspherical Variational Auto-Encoders
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.
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
HAKE is inspired by the fact that concentric circles in the polar coordinate system can naturally reflect the hierarchy.
Translating Embeddings for Modeling Multi-relational Data
We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces.
LINE: Large-scale Information Network Embedding
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.
Complex Embeddings for Simple Link Prediction
In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases.
Convolutional 2D Knowledge Graph Embeddings
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.