It provides some interesting graph embedding techniques based on task-free or task-specific intuitions.
- Pure Network Embedding
- Attributed Network Embedding (Attribute Vectors)
- Attributed Network Embedding (Text Content)
- Graph Neural Networks (Semi-supervised Node Classification)
- Graph Neural Networks (Graph Classification)
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DeepWalk: Online Learning of Social Representations (KDD'14). [Paper] [Python Code]
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LINE: Large-scale Information Network Embedding (WWW'15). [Paper] [C++ Code]
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node2vec: Scalable Feature Learning for Networks (KDD'16). [Paper] [Project][Python Code]
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Label Informed Attributed Network Embedding (WSDM'17). [Paper] [MATLAB Code]
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Accelerated Attributed Network Embedding (SDM'17). [Paper] [Python Code] [MATLAB Code]
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Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking (ICLR'18). [Paper][OpenReview] [Python Code]
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Network Representation Learning with Rich Text Information (IJCAI'15). [Paper] [MATLAB Code]
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CANE: Context-Aware Network Embedding for Relation Modeling (ACL'17). [Paper] [Python Code]
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Diffusion Maps for Textual Network Embedding (NIPS'18). [Paper] [Python Code]
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Semi-Supervised Classification with Graph Convolutional Networks (ICLR'17). [Paper][OpenReview] [Code]
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FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling (ICLR'18). [Paper][OpenReview] [Code]
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Adaptive Sampling Towards Fast Graph Representation Learning (NIPS'18). [Paper] [Code]
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Stochastic Training of Graph Convolutional Networks with Variance Reduction (ICML'18). [Paper] [Code]
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Graph Attention Networks (ICLR'18). [Paper][OpenReview] [Code]