- 1st week
- Cui, Peng, et al. "A survey on network embedding." IEEE transactions on knowledge and data engineering 31.5 (2018): 833-852.
- Wang, Xiao, et al. "A survey on heterogeneous graph embedding: methods, techniques, applications and sources." IEEE Transactions on Big Data (2022).
- Barros, Claudio DT, et al. "A survey on embedding dynamic graphs." ACM Computing Surveys (CSUR) 55.1 (2021): 1-37.
- 2nd week
- DeepWalk: Online Learning of Social Representations
- node2vec: Scalable Feature Learning for Networks
- LINE: Large-scale information network embedding
- 3rd week
- Asymmetric Transitivity Preserving Graph Embedding
- GraRep: Learning Graph Representations with Global Structural Information
- Structural Deep Network Embedding
- 4th week
- Don’t Walk, Skip! Online Learning of Multi-scale Network Embeddings
- struc2vec: Learning Node Representations from Structural Identity
- subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs
- 5th week
- metapath2vec: Scalable Representation Learning for Heterogeneous Networks
- HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning
- PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks
- 6th week
- Signed Network Embedding in Social Media
- Label Informed Attributed Network Embedding
- Metagraph2vec: complex semantic path augmented heterogeneous network embedding
- 7th week
- Hyperbolic heterogeneous information network embedding
- Shne: Representation learning for semantic-associated heterogeneous networks
- Network schema preserving heterogeneous information network embedding
- 8th week
- ATP: Directed Graph Embedding with Asymmetric Transitivity Preservation
- GEMSEC: Graph Embedding with Self Clustering
- Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
- 9th week
- gl2vec: Learning Feature Representation Using Graphlets for Directed Networks
- role2vec - Learning Role-based Graph Embeddings
- gat2vec: Representation learning for attributed graphs
- 10th week
- Translating Embeddings for Modeling Multi-relational Data
- Learning Entity and Relation Embeddings for Knowledge Graph Completion
- Knowledge Graph Embedding by Translating on Hyperplanes
- 11th week
- Embedding Entities and Relations for Learning and Inference in Knowledge Bases
- Complex Embeddings for Simple Link Prediction
- Text-Enhanced Representation Learning for Knowledge Graph
Graphs illustrate intricate patterns in our perception of the world and ourselves; graph mining enhances this comprehension by highlighting overlooked details.