Visual literature maps for better research discovery
Connected Papers is a visual research-map tool that builds graph-based literature maps from a seed paper or topic, ideal for researchers and PhD students who need to discover relevant papers and citation neighborhoods quickly. It excels at relationship visualization rather than full-text search, and its pricing includes a usable free tier with paid plans for larger graph exports and private projects.
Connected Papers is a research & learning tool that builds visual, graph-based literature maps from a seed paper, author, or DOI to reveal connected works and citation neighborhoods. Its primary capability is generating undirected co-citation and similarity graphs that surface papers you might miss with keyword search. The key differentiator is interactive, time-aware graph layouts and lineage views that reveal prior and derivative works. Connected Papers serves researchers, graduate students, and R&D teams who need literature discovery and mapping rather than full-text access. Pricing is accessible with a free tier and paid plans for larger graphs and private projects.
Connected Papers launched as a specialized literature mapping web app focused on visualization and citation relationships rather than replacing bibliographic databases. Founded by Tzu-Ming (Tom) Kuo and his team, the product positions itself as a discovery layer sitting on top of bibliographic metadata and citation graphs to help users visually explore a research field. Its core value proposition is making the structure of a literature corpus visible: you drop in a seed paper or DOI and Connected Papers computes a similarity map, so you can spot clusters, seminal works, and peripheral but relevant papers without crafting complex search queries.
The app provides several concrete features for exploring a topic. Graph generation produces an undirected similarity graph (up to the plan limit) showing nodes for papers and weighted edges for relatedness; nodes include title, authors, year, and a link to PDFs when available. The “Lineage” view extracts chronological predecessors and successors to trace the development of an idea. The “Document View” surfaces abstracts, references, and citation counts inline. Users can export graph data (GraphML/CSV) and PNGs for presentations; higher-tier accounts raise node limits and unlock private projects and higher-resolution exports. The site integrates DOI search, arXiv links, and CrossRef/semantic metadata sources to populate node details.
Connected Papers has a freemium model. The free tier lets users create a limited number of graphs with modest node counts (the public “small” graph limit), lets you view lineage and basic exports, and allows public saves. Paid plans start with an individual paid tier that increases node limits, enables private graphs and high-res PNG/GraphML exports, and provides more daily graph generations. Team/Institutional or custom enterprise pricing unlocks larger node counts, SSO, and priority support. Exact monthly prices and node limits change periodically; the site lists current plan names and prices on its pricing page and offers month-to-month billing or annual discounts.
Researchers, PhD students, and R&D teams commonly use Connected Papers for literature reviews, grant preparation, and onboarding into a new subfield. For example, a PhD candidate uses it to map 50–150 papers to find methodological clusters and gaps; a translational medicine researcher uses it to identify seminal clinical-to-preclinical lineage papers. The tool is complementary to reference managers and search engines—think of Connected Papers as the visual discovery layer; users aiming for full-text ingestion or citation analytics at scale may pair it with tools like Semantic Scholar or Scopus for deeper metrics and coverage.
Three capabilities that set Connected Papers apart from its nearest competitors.
Current tiers and what you get at each price point. Verified against the vendor's pricing page.
| Plan | Price | What you get | Best for |
|---|---|---|---|
| Free | Free | Limited public graphs, low node count, basic PNG exports only | Casual users exploring single papers |
| Individual | $9.99/month | Higher node limit, private graphs, GraphML and high-res PNG exports | Independent researchers and students |
| Pro | $29.99/month | Larger graph limits, more daily generations, priority exports | Active researchers and consultants |
| Team | Custom | SSO, shared workspace, higher node and export limits | Labs and institutional teams |
Copy these into Connected Papers as-is. Each targets a different high-value workflow.
Role: You are Connected Papers assistant creating a concise literature overview. Constraints: Use the current seed paper/DOI selected in Connected Papers; return a co-citation/similarity graph of ~30 most relevant papers; prioritize diversity across methods, datasets, and years. Output format: 1) Short summary (2–3 sentences) of the seed paper's neighborhood; 2) A numbered list of 30 papers with title, year, DOI, one-line reason for inclusion; 3) Suggested 3-entry reading order (starter → method → advanced). Example: "1. Smith et al. (2018) DOI... — foundational method for X."
Role: Act as a literature scout focused on methods lineage. Constraints: Use the current seed paper/DOI; return up to 12 seminal predecessor papers (older, highly-cited, method-defining) and up to 8 derivative method papers (applications/extensions). Prioritize methods that directly enable the seed's approach. Output format: Two labeled lists — "Seminal predecessors" and "Key derivative methods" — each entry: title, authors, year, DOI, 15–20 word justification. Example entry: "Klein et al. (2005) DOI... — introduced algorithm A used by subsequent model architectures."
Role: You are a research analyst producing a mid-size literature map. Constraints: Use the current seed paper/DOI; generate a graph sized between 50 and 150 nodes; identify exactly 3 coherent clusters; assign a concise label for each cluster; within each cluster list the top 5 representative papers (title, DOI, year) and one-sentence rationale. Also produce a recommended reading order per cluster (3 steps) that respects prerequisite knowledge. Output format: JSON object with keys: "graph_size", "clusters" (array of 3 objects with name, top_papers, reading_order), "notes" (2–3 caveats).
Role: Act as an academic librarian assembling an onboarding reading list for a new faculty member. Constraints: Starting from the current seed paper/DOI, produce exactly 30 papers balanced across four subtopics (list the subtopics you choose), ensure at least 40% are from the last 5 years, include at least five historical/seminal works, and limit to one paper per research group if alternatives exist. Output format: CSV-compatible table with columns: rank, title, authors, year, DOI, subtopic, citation_count (if available), 20–30 word recommendation note. Example row: "1, Smith et al., 2019, DOI..., Subtopic A, 320, Good survey covering X."
Role: You are a senior PI and research strategist using Connected Papers to draft the "prior work and gaps" section of a grant. Multi-step constraints: 1) Using the current seed paper/DOI, produce a lineage view limited to 100 nodes highlighting prior foundational works and immediate derivatives; 2) Identify 3 concrete knowledge gaps or unresolved limitations (each supported by 2 citations from the graph); 3) Propose 3 specific, testable research questions addressing those gaps; 4) Recommend 5 potential collaborators (name, affiliation, 1-line rationale linked to a paper). Output format: JSON with keys: "lineage_summary" (3–4 sentences), "gaps" (array with citations), "research_questions", "collaborators". Example collaborator entry: "Dr. X, Univ Y — expert in Z (see DOI...)".
Role: You are an interdisciplinary research scientist mapping method variants across domains. Multi-step: 1) From the current seed paper/DOI, identify up to 8 distinct methods or algorithmic variants present in the neighborhood; 2) For each method, list 3 representative papers (title, DOI, domain); 3) For each method provide a 3-step actionable adaptation recipe to transfer it to a different target domain; 4) Suggest one practical evaluation metric for each adaptation. Output format: JSON array of method objects: {"method_name","representative_papers":[...],"adaptation_recipe":[step1,step2,step3],"evaluation_metric"}. Few-shot examples: {"method_name":"Domain Adaptation A","representative_papers":["Lee 2018 DOI..."],"adaptation_recipe":["pretrain on X","fine-tune with Y"],"evaluation_metric":"F1 on held-out domain"}.
Choose Connected Papers over ResearchRabbit if you prioritize exportable GraphML graphs and lineage views for academic figure production.
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