The application below is more a use of natural language processing to generate graph dings than the other way around. However it is the traversal of a graph that enables the generation of dings using an unsupervised model. The use case is transformation of structured into unstructured sentence form that enables generation of graph dings which can then be used for a variety of tasks. Input could also be rdf triples with each triple flattened to two edges - to construct a knowledge graph with nodes being represented as dings. For instance if we have a two column input representing the edges of a graph (shown below) we can convert this into sentences either by treating it as a directed or undirected graph (the s 1572 172 There are C++ and python implementations of node2vec that are available node2vec s Python implementation s C++ implementation s for large graphs the word2vec part of the implementation in node2vec may not work at times. However the graph generation works - so it can be isolated and used. We can just use standard word2vec once the sentences are generated 1