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We study how follow edges, review quality, graph proximity and freshness can be blended into an explainable feed optimized for high-signal research discovery.
We combine publication metadata, author neighborhoods and dataset signals to improve retrieval quality for computational biology teams working across papers, code and experiments.
We evaluate rubric design for structured reviews, moderation queues and reviewer calibration in technical communities.
We propose ledger-based moderation records that improve accountability, appeals and policy learning in research products.
We extract actionable collaboration intent from follows, saves, reviews and topic overlap without collapsing trust into a single opaque metric.
We present a graph-native product architecture for scholarly sensemaking where work pages, topic maps and researcher trust signals reduce time-to-understanding for new literature.
The work drawing the strongest visible discussion right now.
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Core graph coverage and claimed identity on the platform.
Public profiles building visible momentum through reviews, comments and linked work.
