Topic overview
Entity resolution, graph indexing and scholarly discovery.
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11 works
Papers in this area
We combine publication metadata, author neighborhoods and dataset signals to improve retrieval quality for computational biology teams working across papers, code and experiments.
We trace dataset reuse across scholarly graphs to improve auditability, discovery and replication planning.
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.
We align code repositories, datasets and papers with provenance-aware linking that survives version churn and renamed assets.
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 show that persistent graph neighborhoods and operator-facing retrieval memory improve literature navigation across papers, people and opportunities.
We compare dense and sparse retrieval strategies for scholarly search when operators need interpretable reasons and controllable ranking.
We extract actionable collaboration intent from follows, saves, reviews and topic overlap without collapsing trust into a single opaque metric.
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 collaborateComputational Biology Fellow
Curates multimodal biology benchmarks and literature-aware evaluation pipelines.
Heads-down modeIndustry 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