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A PyTorch implementation of "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective" (WWW 2019)
A PyTorch implementation of "Signed Graph Convolutional Network" (ICDM 2018).
A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018).
A PyTorch Implementation of "SINE: Scalable Incomplete Network Embedding" (ICDM 2018).
DeepWalk - Deep Learning for Graphs
The main features of phanein/deepwalk are: Embedding Algorithms, Graph Neural Networks.
Projects with overlapping indexed features include: benedekrozemberczki/sgcn — A PyTorch implementation of "Signed Graph Convolutional Network" (ICDM 2018). benedekrozemberczki/splitter — A Pytorch implementation of "Splitter: Learning Node Representations that Capture Multiple Social Contexts" (WWW 2019). benedekrozemberczki/attentionwalk — A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018). benedekrozemberczki/seal-ci — A PyTorch implementation of "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective" (WWW 2019). benedekrozemberczki/sine — A PyTorch Implementation of "SINE: Scalable Incomplete Network Embedding" (ICDM 2018). tangjianpku/line — LINE: Large-scale information network embedding.