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Found 6 papers, 6 papers with code
Jointly Learnable Data Augmentations for Self-Supervised GNNs
Recently, a number of SSL methods for graph representation learning have achieved performance comparable to SOTA semi-supervised GNNs.
Self-supervised Graph Neural Networks without explicit negative sampling
This calls for unsupervised learning techniques that are powerful enough to achieve comparable results as semi-supervised/supervised techniques.
Dynamic Embeddings for Interaction Prediction
In recommender systems (RSs), predicting the next item that a user interacts with is critical for user retention.
Gossip and Attend: Context-Sensitive Graph Representation Learning
In this study we show that in-order to extract high-quality context-sensitive node representations it is not needed to rely on supplementary node features, nor to employ computationally heavy and complex models.
Which way? Direction-Aware Attributed Graph Embedding
Most studies ignore the directionality, so as to learn high-quality representations optimized for node classification.
Graph Neighborhood Attentive Pooling
Network representation learning (NRL) is a powerful technique for learning low-dimensional vector representation of high-dimensional and sparse graphs.
