This week's book giveaway is in the Reactive Progamming forum. We're giving away four copies of Reactive Streams in Java: Concurrency with RxJava, Reactor, and Akka Streams and have Adam Davis on-line! See this thread for details.
I'm not sure I totally understand your question, so let me know if I don't answer it.
Our focus is on graph algorithms that can be used for analysis or feature engineering. We've seen graph features greatly improve the ROC curves for ML predictions and knowledge graphs help for adding more context to AI systems. In regards to features for training ML models, we see a lot of use of different community detection algorithms for fraud detection, centrality algos for finding influencer for recommendations, and a mix for disambiguations. We also see quite a bit of use similarity and link prediction algorithms for feature engineering.