Link Prediction
808 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 )
Benchmarks
These leaderboards are used to track progress in Link Prediction
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Libraries
Use these libraries to find Link Prediction models and implementations
IMDb Movie Reviews
MovieLens
Pubmed
OGB
FB15k
Cora
YAGO
Subtasks
Latest papers with no code
Multi-Relational Graph Neural Network for Out-of-Domain Link Prediction
Dynamic multi-relational graphs are an expressive relational representation for data enclosing entities and relations of different types, and where relationships are allowed to vary in time.
xLP: Explainable Link Prediction for Master Data Management
Explaining neural model predictions to users requires creativity.
Link Prediction for Social Networks using Representation Learning and Heuristic-based Features
Here, we explore various feature extraction techniques to generate representations of nodes and edges in a social network that allow us to predict missing links.
Knowledge Graph Large Language Model (KG-LLM) for Link Prediction
The task of predicting multiple links within knowledge graphs (KGs) stands as a challenge in the field of knowledge graph analysis, a challenge increasingly resolvable due to advancements in natural language processing (NLP) and KG embedding techniques.
A Differential Geometric View and Explainability of GNN on Evolving Graphs
Graphs are ubiquitous in social networks and biochemistry, where Graph Neural Networks (GNN) are the state-of-the-art models for prediction.
From Chain to Tree: Refining Chain-like Rules into Tree-like Rules on Knowledge Graphs
With good explanatory power and controllability, rule-based methods play an important role in many tasks such as knowledge reasoning and decision support.
In-n-Out: Calibrating Graph Neural Networks for Link Prediction
While networks for tabular or image data are usually overconfident, recent works have shown that graph neural networks (GNNs) show the opposite behavior for node-level classification.
Unveiling Hidden Links Between Unseen Security Entities
The proliferation of software vulnerabilities poses a significant challenge for security databases and analysts tasked with their timely identification, classification, and remediation.
RCoCo: Contrastive Collective Link Prediction across Multiplex Network in Riemannian Space
Annotating anchor users is laborious and expensive, and thus it is impractical to work with quantities of anchor users.
Scaling up Dynamic Edge Partition Models via Stochastic Gradient MCMC
For large network data, we propose a stochastic gradient Markov chain Monte Carlo (SG-MCMC) algorithm for scalable inference in the proposed model.
