Graph Neighborhoods for Scholarly Search and Topic Expansion
We precompute neighborhoods over works, authors, communities and institutions to speed exploratory search.
Topic overview
Signals, incentives and matching for scholarly collaboration.
Topic snapshot
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9 works
Papers in this area
We precompute neighborhoods over works, authors, communities and institutions to speed exploratory search.
This work formalizes a claim workflow that separates platform users, researcher profiles and author entities, improving identity resolution without degrading product usability.
This paper combines institution verification, topical expertise and trust snapshots to route methodological questions to the right specialists.
We propose ledger-based moderation records that improve accountability, appeals and policy learning in research products.
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.
We evaluate rubric design for structured reviews, moderation queues and reviewer calibration in technical communities.
We extract actionable collaboration intent from follows, saves, reviews and topic overlap without collapsing trust into a single opaque metric.
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 use graph signals and topical expertise to connect researchers with fellowships, residencies and collaboration openings.
People in this topic
Applied Research Engineer
Builds collaboration tooling and graph-backed ranking loops for scholarly platforms.
Open to collaborateSenior Lecturer
Works on review quality, moderation design and institutional trust systems for online science.
Open to collaborateResearch Scientist, Networked Intelligence
Designs graph retrieval layers and provenance-rich interfaces for scientific search.
Open to collaboratePI, Machine Intelligence Lab
Builds graph-native systems for research evaluation and discovery.
Open to collaborateStaff Research Scientist
Studies expert routing, retrieval systems and opportunity matching in scientific networks.
Open to collaboratePostdoctoral Researcher
Works on literature-scale retrieval and scientific representation learning.
Open to collaboratePhD Candidate
Studies trust signals and structured peer feedback in online science.
Open to collaborateIndustry Research Scientist
Bridges dataset-centric ML with reproducibility tooling.
Heads-down modeResearch Software Engineer
Links papers, repositories and datasets with reproducible provenance infrastructure.
Open to collaborate