Inductive Link Prediction
20 papers with code • 3 benchmarks • 3 datasets
In inductive link prediction inference is performed on a new, unseen graph whereas classical transductive link prediction performs both training and inference on the same graph.
Benchmarks
These leaderboards are used to track progress in Inductive Link Prediction
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ILPC22-Small
Most implemented papers
Inductive Entity Representations from Text via Link Prediction
dfdazac/blp
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However, the extent to which these representations learned for link prediction generalize to other tasks is unclear.
Improving Inductive Link Prediction Using Hyper-Relational Facts
mali-git/hyper_relational_ilp
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In this work, we classify different inductive settings and study the benefits of employing hyper-relational KGs on a wide range of semi- and fully inductive link prediction tasks powered by recent advancements in graph neural networks.
Neighborhood-aware Scalable Temporal Network Representation Learning
graph-com/neighborhood-aware-temporal-network
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Such a dictionary representation records a downsampled set of the neighboring nodes as keys, and allows fast construction of structural features for a joint neighborhood of multiple nodes.
DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs
alisadeghian/DRUM
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NeurIPS 2019
Despite the importance of inductive link prediction, most previous works focused on transductive link prediction and cannot manage previously unseen entities.
Inductive Link Prediction for Nodes Having Only Attribute Information
In attributed graphs, both the structure and attribute information can be utilized for link prediction.
Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs
TACT is inspired by the observation that the semantic correlation between two relations is highly correlated to their topological structure in knowledge graphs.
How Neural Processes Improve Graph Link Prediction
Link prediction is a fundamental problem in graph data analysis.
An Open Challenge for Inductive Link Prediction on Knowledge Graphs
pykeen/ilpc2022
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An emerging trend in representation learning over knowledge graphs (KGs) moves beyond transductive link prediction tasks over a fixed set of known entities in favor of inductive tasks that imply training on one graph and performing inference over a new graph with unseen entities.
Subgraph Neighboring Relations Infomax for Inductive Link Prediction on Knowledge Graphs
Tebmer/SNRI
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Inductive link prediction for knowledge graph aims at predicting missing links between unseen entities, those not shown in training stage.
Disconnected Emerging Knowledge Graph Oriented Inductive Link Prediction
ninecl/dekg-ilp
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A more challenging scenario is that emerging KGs consist of only unseen entities, called as disconnected emerging KGs (DEKGs).
