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Graph Embedding
Graphs are essential tools to capture and model complicated relationships among data. In a variety of graph applications, including protein-protein interaction networks, social... -
Adversarially Regularized Graph Autoencoder for Graph Embedding
Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving... -
Graph-Structured Data Dataset
This dataset contains graph-structured data and its corresponding features. -
Cora, CiteSeer, PubMed, CS, Photo
Citation networks (Cora, CiteSeer, PubMed) and coauthor network (CS) and co-purchase graph (Photo) -
IMM ATT UMIST, COIL USPS, and PIE datasets
The IMM ATT UMIST, COIL USPS, and PIE datasets are used for node classification tasks. -
Cora and Citeseer datasets
The Cora and Citeseer datasets are used for training machine learning models to classify documents into different categories. -
Unsupervised Graph Embedding via Adaptive Graph Learning
Graph autoencoders (GAEs) are powerful tools in representation learning for graph embedding. However, the performance of GAEs is very dependent on the quality of the graph... -
Ocean Drifter Complex
The Ocean Drifter Complex is a dataset formed by using simplicial embedding to represent real-world ocean drifters. The dataset is used to test the performance of an algorithm... -
Citation Complex
The Citation Complex is a dataset formed by using simplicial embedding to represent real-world citations. The dataset is used to test the performance of an algorithm on...
