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 )
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
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.
A Relation-Interactive Approach for Message Passing in Hyper-relational Knowledge Graphs
Overall, ReSaE gives a encoding solution for hyper-relational KGs and ensures stronger performance on downstream link prediction tasks.
Automating Psychological Hypothesis Generation with AI: Large Language Models Meet Causal Graph
Leveraging the synergy between causal knowledge graphs and a large language model (LLM), our study introduces a groundbreaking approach for computational hypothesis generation in psychology.
Link Prediction under Heterophily: A Physics-Inspired Graph Neural Network Approach
Physics-Inspired GNNs such as GRAFF provided a significant contribution to enhance node classification performance under heterophily, thanks to the adoption of physics biases in the message-passing.
Expressive Higher-Order Link Prediction through Hypergraph Symmetry Breaking
Higher-order link prediction is the task of predicting the existence of a missing hyperedge in a hypergraph.
A Condensed Transition Graph Framework for Zero-shot Link Prediction with Large Language Models
To address this, in this work, we introduce a Condensed Transition Graph Framework for Zero-Shot Link Prediction (CTLP), which encodes all the paths' information in linear time complexity to predict unseen relations between entities, attaining both efficiency and information preservation.
Class-Balanced and Reinforced Active Learning on Graphs
It learns an optimal policy to acquire class-balanced and informative nodes for annotation, maximizing the performance of GNNs trained with selected labeled nodes.
Node Duplication Improves Cold-start Link Prediction
Graph Neural Networks (GNNs) are prominent in graph machine learning and have shown state-of-the-art performance in Link Prediction (LP) tasks.
