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Found 5 papers, 2 papers with code
Graph Contrastive Learning with Generative Adversarial Network
Specifically, we present GACN, a novel Generative Adversarial Contrastive learning Network for graph representation learning.
PANE-GNN: Unifying Positive and Negative Edges in Graph Neural Networks for Recommendation
Recommender systems play a crucial role in addressing the issue of information overload by delivering personalized recommendations to users.
Instant Representation Learning for Recommendation over Large Dynamic Graphs
Furthermore, the Neighborhood Disturbance existing in dynamic graphs deteriorates the performance of neighbor-aggregation based graph models.
Multi-behavior Self-supervised Learning for Recommendation
Secondly, existing methods utilizing self-supervised learning (SSL) to tackle the data sparsity neglect the serious optimization imbalance between the SSL task and the target task.
HybridGNN: Learning Hybrid Representation in Multiplex Heterogeneous Networks
One of the most important tasks in recommender systems is to predict the potential connection between two nodes under a specific edge type (i. e., relationship).
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