Graph Embeddings
node2vec
Introduced by Grover et al. in node2vec: Scalable Feature Learning for Networksnode2vec is a framework for learning graph embeddings for nodes in graphs. Node2vec maximizes a likelihood objective over mappings which preserve neighbourhood distances in higher dimensional spaces. From an algorithm design perspective, node2vec exploits the freedom to define neighbourhoods for nodes and provide an explanation for the effect of the choice of neighborhood on the learned representations.
For each node, node2vec simulates biased random walks based on an efficient network-aware search strategy and the nodes appearing in the random walk define neighbourhoods. The search strategy accounts for the relative influence nodes exert in a network. It also generalizes prior work alluding to naive search strategies by providing flexibility in exploring neighborhoods.
Source: node2vec: Scalable Feature Learning for Networks
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Link Prediction | 23 | 15.75% |
| Node Classification | 15 | 10.27% |
| Graph Embedding | 14 | 9.59% |
| Clustering | 7 | 4.79% |
| Recommendation Systems | 7 | 4.79% |
| Graph Representation Learning | 7 | 4.79% |
| BIG-bench Machine Learning | 7 | 4.79% |
| Community Detection | 6 | 4.11% |
| General Classification | 6 | 4.11% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
