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The cognitive manifold of published content is currently expanding in allareas of science. However, Scientific Knowledge Graphs (SKGs) only provide poorpictures of the adversarial directions and scientific controversies that feedthe production of knowledge. In this Article, we tackle the understanding ofthe design of the information space of a cognitive representation of researchactivities, and of related bottlenecks that affect search interfaces, in themapping of structured objects into graphs. We propose, with SKG GRAPHYP, anovel graph designed geometric architecture which optimizes both the detectionof the knowledge manifold of "cognitive communities", and the representation ofalternative paths to adversarial answers to a research question, for instancein the context of academic disputes. With a methodology for designing "ManifoldSubnetworks of Cognitive Communities", GRAPHYP provides a classification ofdistinct search paths in a research field. Users are detected from the varietyof their search practices and classified in "Cognitive communities" from theanalysis of the search history of their logs of scientific documentation. Themanifold of practices is expressed from metrics of differentiated uses bytriplets of nodes shaped into symmetrical graph subnetworks, with the followingthree parameters: Mass, Intensity, and Variety.

GRAPHYP: A Scientific Knowledge Graph with Manifold Subnetworks of Communities. Detection of Scholarly Disputes in Adversarial Information Routes