Advanced network use cases
In Chapter 1, Introducing Natural Language Processing, I specified several advanced use cases for NLP, such as language translation and text generation. However, while thinking about network analysis, my mind immediately asked, what would an advanced network use case even mean? This is all pretty advanced stuff. With NLP, you have simple tasks, such as tokenization, lemmatization, and simple sentiment analysis (positive or negative, hate speech or not hate speech), and you have advanced tasks. With networks, I can think of three potentially advanced use cases:
- Graph ML
- Knowledge graphs
- Recommendation systems
However, I don’t think of any of them as all that advanced. I think of them as just having different implementations from other things I have mentioned. Furthermore, just because something is more technically challenging does not make it advanced or more important. In fact, if it is more difficult and returns less useful...
