This chapter provided an overview of the graph embedding algorithms. Starting with adjacency-based methods using similarity metrics, we moved to a neural network-based approach. After gaining an understanding of the skip-graph model using word embedding as an example, we drew a parallel with graphs using DeepWalk to generate sentences. We also studied a variant of DeepWalk called node2vec, where the traversal is configured by two parameters to enhance local or global graph structures. The following table provides a short summary of the assumption about the graph structure made in each of the algorithms studied:
| Algorithm | Hypothesis |
| Adjacency matrix | The higher the weight of the edge between nodes i and j, the more similar nodes i and j are. |
| LLE | Node embedding is a linear combination of its neighbors' embeddings. |
| HOPE | Similarity between nodes in the graph can be measured by a metric such as the Adamic-Adar score. |
| DeepWalk | The similarity between two nodes is given... |
