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Found 7 papers, 3 papers with code
CORE: Data Augmentation for Link Prediction via Information Bottleneck
Link prediction (LP) is a fundamental task in graph representation learning, with numerous applications in diverse domains.
You do not have to train Graph Neural Networks at all on text-attributed graphs
Graph structured data, specifically text-attributed graphs (TAG), effectively represent relationships among varied entities.
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.
Universal Link Predictor By In-Context Learning on Graphs
In this work, we introduce the Universal Link Predictor (UniLP), a novel model that combines the generalizability of heuristic approaches with the pattern learning capabilities of parametric models.
Pure Message Passing Can Estimate Common Neighbor for Link Prediction
This discrepancy stems from a fundamental limitation: while MPNNs excel in node-level representation, they stumble with encoding the joint structural features essential to link prediction, like CN.
Ranked #1 on
Link Property Prediction
on ogbl-citation2
FakeEdge: Alleviate Dataset Shift in Link Prediction
In this paper, we first identify the dataset shift problem in the link prediction task and provide theoretical analyses on how existing link prediction methods are vulnerable to it.
Heterogeneous Graph Masked Autoencoders
In light of this, we study the problem of generative SSL on heterogeneous graphs and propose HGMAE, a novel heterogeneous graph masked autoencoder model to address these challenges.
