Datalog <> graph is classic, the points-to analysis papers were some of my first "aha"s for why it's practical If you enjoy this kind of thinking, we recently

Datalog <> graph is classic, the points-to analysis papers were some of my first "aha"s for why it's practical If you enjoy this kind of thinking, we recently released GFQL for dataframe-native accelerated graph querying & compute that build on some of the under-the-hood insights here Imagine Neo4j Cypher, except no need for a database -- just import it -- and automatically vectorizes for significantly faster CPU+GPU performance. This is fundamentally similar to the kinds of optimized engines the article's datalog approach enables. In fact, one of our big internal questions was whether to use ~datalog syntax as the frontend! We've run it on 100M+ edge graphs in seconds on some of the cheapest GPUs you can get, and are getting ready for the next rev with aggregate compute as it's becoming more important for our community: https://github.com/graphistry/pygraphistry/blob/master/demos/more_examples/graphistry_features/hop_and_chain_graph_pattern_mining.ipynb https://github.com/graphistry/pygraphistry/blob/master/demos...