NodeEmbedMethodEnum
Enums containing possible values for node embedding methods.
URI: NodeEmbedMethodEnum
Permissible Values
| Value | Meaning | Description | Info |
|---|---|---|---|
| SkipGram | None | Skip-gram is used to predict the context word for a given target word. It's reverse of CBOW algorithm. | |
| CBOW | None | Continuous Bag of Words Model (CBOW). In the CBOW model, the distributed representations of context (or surrounding words) are combined to predict the word in the middle. | |
| GloVe | None | GloVe is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space. | |
| Siamese | None | A Siamese neural network (sometimes called a twin neural network) is an artificial neural network that uses the same weights while working in tandem on two different input vectors to compute comparable output vectors. | |
| TransE | None | TransE, or Translating Embeddings for Modeling Multi-relational Data, lets us embed the contents of a knowledge graph by assigning vectors to nodes and edge types (a.k.a. predicates) and, for each subject-predicate-object triple, minimizing the distance between the object vector and the translation of the subject vector along the predicate vector. | |
| SimplE | None | None | |
| TransH | None | TransH defines a hyperplane for each relationship r, and uses the translation vector r and normal vector w of the relationship r on the plane to represent it. | |
| TransR | None | None |