Understanding Graph Embeddings

Graph embeddings are becoming increasingly important in Enterprise Knowledge Graph (EKG) strategy as they enable quick finding of similar items in large billion-vertex EKGs. They aid real-time similarity ranking functions in EKG and can be used for recommendation, next best action, and cohort building. Graph embeddings are small data structures that absorb a great deal of information about each item in an EKG and compress it into compact and easy to compare structures. They enable real-time similarity calculations that can be used to classify items in the graph and make real-time recommendations to users. The process of creating a new embedding vector is called "encoding" or "encoding a vertex", while the process of regenerating a vertex from the embedding is called "decoding" or generating a vertex. Graph embeddings work with other graph algorithms, such as clustering or classification, and can be used to increase the performance and quality of these other algorithms.