Search Results for author:
Found 8 papers, 6 papers with code
Representation learning in multiplex graphs: Where and how to fuse information?
In recent years, unsupervised and self-supervised graph representation learning has gained popularity in the research community.
Unveiling the Potential of Probabilistic Embeddings in Self-Supervised Learning
In recent years, self-supervised learning has played a pivotal role in advancing machine learning by allowing models to acquire meaningful representations from unlabeled data.
Graph-level representations using ensemble-based readout functions
Graph machine learning models have been successfully deployed in a variety of application areas.
RAFEN -- Regularized Alignment Framework for Embeddings of Nodes
A well-defined node embedding model should reflect both node features and the graph structure in the final embedding.
On Graph Neural Network Ensembles for Large-Scale Molecular Property Prediction
In order to advance large-scale graph machine learning, the Open Graph Benchmark Large Scale Challenge (OGB-LSC) was proposed at the KDD Cup 2021.
Graph Barlow Twins: A self-supervised representation learning framework for graphs
The self-supervised learning (SSL) paradigm is an essential exploration area, which tries to eliminate the need for expensive data labeling.
AttrE2vec: Unsupervised Attributed Edge Representation Learning
Representation learning has overcome the often arduous and manual featurization of networks through (unsupervised) feature learning as it results in embeddings that can apply to a variety of downstream learning tasks.
FILDNE: A Framework for Incremental Learning of Dynamic Networks Embeddings
Experimental results on several downstream tasks, over seven real-world data sets, show that FILDNE is able to reduce memory and computational time costs while providing competitive quality measure gains with respect to the contemporary methods for representation learning on dynamic graphs.
