In an earlier post, I had stated that the recent advances in Natural Language Processing (NLP) technology can be, to a large extent, attributed to the use of very high-dimensional vectors for language representation. These high-dimensional, 764 dimensions is common, vector representations are called embeddings and are aimed at capturing semantic meaning and relationships between linguistic items. Given that graphs are everywhere, it is not surprising to see the ideas of word and sentence embeddings being extended to graphs in the form of node embeddings. What are Node Embedding? Node embeddings are encodings of the properties and relationships of nodes in a low-dimensional vector space. This enables nodes with similar properties or connectivity patterns to have similar vector representations. Using node embeddings can improve performance on various graph analytics tasks such as node classification, link prediction, and clustering. Methods for Node Embeddings There are seve
A blog about assorted topics from machine learning including deep learning, natural language processing and graph-based methods.