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Trust-Aware Feed Ranking for Scholarly Collaboration Networks

We study how follow edges, review quality, graph proximity and freshness can be blended into an explainable feed optimized for high-signal research discovery.

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12visible nodes
30visible edges
Trust-Aware Feed Ranking for Scholarly Collaboration Networkscurrent focus
2public views

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2topic
5work
2author
3community

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12 ranked node(s)

topic: 2work: 5author: 2community: 3
Drag to move the map, use wheel or controls to zoom.Selected work: Trust-Aware Feed Ranking for Scholarly Collaboration Networks
topicworkauthorcommunityTrust-Aware Feed Ranking ...Workshop Paper / 2024Calibrated Review Rubrics...Workshop Paper / 2025A Signal Layer for Scienc...Preprint / 2026Open-Weight Biology Assis...Preprint / 2026Rhea PatelPhD CandidateSunwoo KimIndustry Research S...Topic-Aware Opportunity M...Preprint / 2026ETH Machine Intelligence LablabMIT Literature SystemsinstitutionTrusted Review Circleinvite onlyResearch Collaboration9 worksLarge Language Models7 works
worksignal 106 adjacent

Trust-Aware Feed Ranking for Scholarly Collaboration Networks

Workshop Paper / 2024

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authorsignal 5
Sunwoo KimIndustry Research Scientist

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2 public view(s)