{"uid":"cap_5N0c8u8qRAk2Ff9stZXto","slug":"genexxis-research-citation-neighbourhood-467ec859","name":"Genexxis Research Citation Neighbourhood","description":"Citation neighbourhood of a seed paper (by DOI, arXiv id, OpenAlex id or query): the seed, OpenAlex related works, most-cited citing works and most-cited references, each with full paper fields.","url":"https://research.genexxis.com/related-papers?utm_source=zero.xyz","method":"GET","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method"],"properties":{"type":{"type":"string","const":"http"},"method":{"enum":["GET"],"type":"string"},"queryParams":{"type":"object","properties":{"q":{"type":"string","description":"Title or keywords; the best match is used as seed"},"doi":{"type":"string","description":"DOI, e.g. 10.48550/arXiv.1706.03762"},"arxiv":{"type":"string","description":"arXiv id, e.g. 1706.03762"},"maxItems":{"type":"string","description":"1-15 per list, default 10 (higher values are capped)"},"openalex":{"type":"string","description":"OpenAlex work id, e.g. W2626778328"}}}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object"}}}}},"responseSchema":{"type":"json","example":{"seed":{"doi":"10.48550/arxiv.1706.03762","type":"preprint","year":2017,"links":{"doi":"https://doi.org/10.48550/arxiv.1706.03762","arxiv":"https://arxiv.org/abs/1706.03762","openalex":"https://openalex.org/W2626778328"},"title":"Attention Is All You Need","venue":"arXiv","topics":["Natural Language Processing Techniques","Topic Modeling","Speech Recognition and Synthesis"],"arxivId":"1706.03762","authors":["Ashish Vaswani","Noam Shazeer","Niki Parmar"],"foundIn":["openalex","arxiv"],"abstract":"The dominant sequence transduction models are based on complex recurrent or convolutional neural networks...","openAccess":{"isOa":true,"pdfUrl":"https://arxiv.org/pdf/1706.03762","status":"green","landingUrl":"https://arxiv.org/abs/1706.03762"},"openalexId":"W2626778328","authorsTotal":8,"citedByCount":78297,"publicationDate":"2017-06-12"},"citing":{"count":5,"total":78297,"papers":[{"year":2020,"title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale"}]},"errors":[],"related":{"count":5,"total":20,"papers":[{"year":2018,"title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","citedByCount":52000}]},"references":{"count":5,"total":28,"papers":[{"year":1997,"title":"Long Short-Term Memory"}]}}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.015","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.008/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.008","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.008","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_tR7p6iz8sbA6Ck4RAFgjp","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.008","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Returns a citation neighbourhood around a seed paper (by DOI, arXiv ID, OpenAlex ID, or keyword query), including related works, top citing papers, and top references with full paper metadata.","exampleAgentPrompt":"Can you pull up the citation neighbourhood for the paper '10.48550/arXiv.1706.03762' — I want to see the top related works, the most-cited papers that cite it, and its key references, up to 10 per list?","exampleUseCases":[{"title":"Literature review for a new paper","prompt":"I'm writing a paper on transformer models. Can you get me the citation neighbourhood for arXiv:1706.03762 — the 'Attention Is All You Need' paper — so I can see what the most influential citing papers are and what references it builds on?"},{"title":"Discovering related work by keyword","prompt":"I don't have a DOI handy, but can you find the citation neighbourhood for the paper about 'BERT pre-training of deep bidirectional transformers' — give me up to 10 related works, citing papers, and references?"},{"title":"Tracing influence of a specific paper","prompt":"Using OpenAlex ID W2626778328, show me which papers most cite this work and what other papers OpenAlex considers related — I want to understand the research landscape around it."}],"resultDescription":"Returns a JSON object with: a 'seed' object containing full paper details (title, authors, year, abstract, venue, topics, DOI, arXiv ID, OpenAlex ID, citation count, open-access links); a 'related' array of OpenAlex-related papers with metadata; a 'citing' array of the most-cited papers that cite the seed; a 'references' array of the seed's most-cited references; counts and totals for each group; and any errors encountered during resolution.","failureModes":["Paper not found in OpenAlex, arXiv, or Crossref — returns empty or partial results with error flags","Ambiguous keyword query matches wrong paper as seed — use DOI or arXiv ID for precision","maxItems values above 15 are silently capped at 15","Rate limiting or upstream API unavailability may return incomplete citation sets","DOI format errors cause lookup failure — must include full DOI string"],"whenToPreferThis":"Choose this endpoint when you need the full citation context around a specific paper — not just metadata for the paper itself but also what cites it, what it references, and what OpenAlex considers related. 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