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Found 6 papers, 1 papers with code
Adaptive Pseudo-Siamese Policy Network for Temporal Knowledge Prediction
Specifically, we design the policy network in our model as a pseudo-siamese policy network that consists of two sub-policy networks.
MixKG: Mixing for harder negative samples in knowledge graph
The representations of entities and relations are learned via contrasting the positive and negative triplets.
Knowledge graph enhanced recommender system
This is attributed to the rich attribute information contained in KG to improve item and user representations as side information.
Multi-Level Graph Contrastive Learning
In this paper, we propose a Multi-Level Graph Contrastive Learning (MLGCL) framework for learning robust representation of graph data by contrasting space views of graphs.
Tucker decomposition-based Temporal Knowledge Graph Completion
Developing the model for temporal knowledge graphs completion is an increasingly important task.
Self-supervised Graph Representation Learning via Bootstrapping
In addition, we summarize three kinds of augmentation methods for graph-structured data and apply them to the DGB.
