Knowledge is created and transmitted through generation. Innovation is often seen as a generative process from collective intelligence, but how does innovation emerges from the blending of accumulated knowledge, and from which path an innovation mostly inherit? A citation network can be seen as a perfect example of a generative process leading to innovation. Inspired by the notion of "stream of knowledge", we propose to look at the question of production of knowledge under the lens of DAGs.

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... ough many works look for the evaluation of publications, we propose to look for production of knowledge within a framework for analyzing DAGs. In this framework inspired by the work of Strahler, we can also account for other well known measures of influence such as the h-index. We propose then to analyze flows of influence in a citation networks as an ascending flow. We propose an efficient dynamic algorithm for integration with modern graph databases, conducting our experiment with the Arxiv HEP-TH dataset. Our results validate the use of DAG flows for citation flows and show evidence of the relevance of the h-index.