How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
A PyTorch implementation of "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective" (WWW 2019)
The main features of benedekrozemberczki/seal-ci are: Embedding Algorithms, Graph Neural Networks, Geometric Deep Learning: Graph & Irregular Structures.
Projects with overlapping indexed features include: benedekrozemberczki/sine — A PyTorch Implementation of "SINE: Scalable Incomplete Network Embedding" (ICDM 2018). benedekrozemberczki/splitter — A Pytorch implementation of "Splitter: Learning Node Representations that Capture Multiple Social Contexts" (WWW 2019). benedekrozemberczki/sgcn — A PyTorch implementation of "Signed Graph Convolutional Network" (ICDM 2018). benedekrozemberczki/attentionwalk — A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018). benedekrozemberczki/clustergcn — A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional… benedekrozemberczki/gam — A PyTorch implementation of "Graph Classification Using Structural Attention" (KDD 2018).
A PyTorch implementation of "Signed Graph Convolutional Network" (ICDM 2018).
A PyTorch Implementation of "SINE: Scalable Incomplete Network Embedding" (ICDM 2018).
A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018).
A Pytorch implementation of "Splitter: Learning Node Representations that Capture Multiple Social Contexts" (WWW 2019).