GDS를 활용한 그래프 임베딩

Machine learning models work with numbers, not graph topology. Graph embeddings convert each node into a fixed-length vector that captures its position and neighborhood.

Once nodes are vectors, you can feed them into classifiers, clustering, and similarity search. Similar nodes end up close together in vector space.

Embeddings run on an in-memory graph projection in GDS. First project the nodes and relationships you care about.

FastRP (Fast Random Projection) is a quick, scalable embedding algorithm. It is a great default for large graphs.

Node2Vec uses biased random walks to learn embeddings that balance local and global structure. It is slower but often richer than FastRP.

GraphSAGE is an inductive method: it learns a function that can embed even new nodes not seen during training, using their features.

The embedding dimension trades expressiveness for cost. Common values are 64, 128, or 256. Larger is more detailed but heavier.

Use the write mode to store embeddings as a node property, so other queries and tools can use them.

With vectors stored, compute cosine similarity between embeddings to find nodes that are structurally alike.

Embeddings are powerful features for GDS machine learning pipelines: node classification and link prediction both benefit from them.

Use FastRP for speed at scale, Node2Vec for richer structure, and GraphSAGE when you must embed unseen nodes.

You learned graph embeddings in GDS:

• Embeddings turn nodes into vectors
• FastRP is fast and scalable
• Node2Vec uses biased random walks
• GraphSAGE handles unseen nodes
• Write embeddings back and use them in ML pipelines