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Found 4 papers, 3 papers with code
GOODAT: Towards Test-time Graph Out-of-Distribution Detection
To identify and reject OOD samples with GNNs, recent studies have explored graph OOD detection, often focusing on training a specific model or modifying the data on top of a well-trained GNN.
A Survey on Fairness-aware Recommender Systems
As information filtering services, recommender systems have extremely enriched our daily life by providing personalized suggestions and facilitating people in decision-making, which makes them vital and indispensable to human society in the information era.
Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation
We design a dual-intent network to learn user intent from an attention mechanism and the distribution of historical data respectively, which can simulate users' decision-making process in interacting with a new item.
CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning
As most of the existing graph neural networks yield effective graph representations of a single graph, little effort has been made for jointly learning two graph representations and calculating their similarity score.
