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
- 4.1. Node Classification
- 4.2. Graph Classification
-
DeepWalk: Online Learning of Social Representations (KDD'14). [Paper] [Python Code]
-
LINE: Large-scale Information Network Embedding (WWW'15). [Paper] [C++ Code]
-
node2vec: Scalable Feature Learning for Networks (KDD'16). [Paper] [Project][Python Code]
-
Label Informed Attributed Network Embedding (WSDM'17). [Paper] [MATLAB Code]
-
Accelerated Attributed Network Embedding (SDM'17). [Paper] [Python Code] [MATLAB Code]
-
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking (ICLR'18). [Paper][OpenReview] [Python Code]
-
Network Representation Learning with Rich Text Information (IJCAI'15). [Paper] [MATLAB Code]
-
CANE: Context-Aware Network Embedding for Relation Modeling (ACL'17). [Paper] [Python Code]
-
Diffusion Maps for Textual Network Embedding (NIPS'18). [Paper] [Python Code]
-
Semi-Supervised Classification with Graph Convolutional Networks (ICLR'17). [Paper][OpenReview] [Code]
-
FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling (ICLR'18). [Paper][OpenReview] [Code]
-
Adaptive Sampling Towards Fast Graph Representation Learning (NIPS'18). [Paper] [Code]
-
Stochastic Training of Graph Convolutional Networks with Variance Reduction (ICML'18). [Paper] [Code]
-
Graph Attention Networks (ICLR'18). [Paper][OpenReview] [Code]
-
Learning Convolutional Neural Networks for Graphs (ICML'16). [Paper] [Code]
-
Deriving Neural Architectures from Sequence and Graph Kernels (ICML'17) [Paper] [Code]
-
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks (AAAI'19). [Paper] [Code]
-
How Powerful are Graph Neural Networks? (ICLR'19). [Paper][OpenReview][Code]
-
Capsule Graph Neural Network (ICLR'19). [Paper][OpenReview][Code]