{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/get-graph","entry":"get_graph","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":15,"n_papers_ran":9,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":14,"n_samples_ran":7,"n_samples_fingerprinted":1,"n_places":16,"n_places_pointer_only":11,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":3,"ran_fixture":0,"ran":3,"unverified":7},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2605.17364","paper":"/paper/arxiv-2605-17364","title":"NewsLens: A Multi-Agent Framework for Adversarial News Bias Navigation","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"joyboseroy/newslens","path":"newsbiasagents/graph/falkor_writer.py","file_url":"https://github.com/joyboseroy/newslens/blob/HEAD/newsbiasagents/graph/falkor_writer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"73d692dfec720072","mcp_get_code":{"code_sha256":"73d692dfec720072"}},{"arxiv_id":"2409.03127","paper":"/paper/fast-algorithms-to-improve-fair-information","title":"Fast algorithms to improve fair information access in networks","date":null,"month_inferred_from_arxiv_id":"2024-09","title_source":"archive","repo":"rhythmthief/FairnessNetworks","path":"code/networks.py","file_url":"https://github.com/rhythmthief/FairnessNetworks/blob/HEAD/code/networks.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"2ff1d172db7d1764","mcp_get_code":{"code_sha256":"2ff1d172db7d1764"}},{"arxiv_id":"2405.03188","paper":"/paper/hyperbolic-geometric-latent-diffusion-model","title":"Hyperbolic Geometric Latent Diffusion Model for Graph Generation","date":"2024-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ringbdstack/hypdiff","path":"diff.py","file_url":"https://github.com/ringbdstack/hypdiff/blob/HEAD/diff.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"135fa8e6cdb079c7","mcp_get_code":{"code_sha256":"135fa8e6cdb079c7"}},{"arxiv_id":"2403.10958","paper":"/paper/efficient-algorithms-for-complexes-of","title":"Efficient Algorithms for Complexes of Persistence Modules with Applications","date":null,"month_inferred_from_arxiv_id":"2024-03","title_source":"archive","repo":"tda-jyamiti/algos-cplxs-pers-modules","path":"main_graph.py","file_url":"https://github.com/tda-jyamiti/algos-cplxs-pers-modules/blob/HEAD/main_graph.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8eef36561c9e1363","mcp_get_code":{"code_sha256":"8eef36561c9e1363"}},{"arxiv_id":"2310.12565","paper":"/paper/open-world-lifelong-graph-learning","title":"Open-World Lifelong Graph Learning","date":"2023-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bobowner/open-world-lgl","path":"load_dataset.py","file_url":"https://github.com/bobowner/open-world-lgl/blob/HEAD/load_dataset.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e2bef084b2ed04a7","mcp_get_code":{"code_sha256":"e2bef084b2ed04a7"}},{"arxiv_id":"2211.13287","paper":"/paper/housediffusion-vector-floorplan-generation","title":"HouseDiffusion: Vector Floorplan Generation via a Diffusion Model with Discrete and Continuous Denoising","date":"2022-11-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aminshabani/house_diffusion","path":"scripts/image_sample.py","file_url":"https://github.com/aminshabani/house_diffusion/blob/HEAD/scripts/image_sample.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"50fe37ab9e685bd6","mcp_get_code":{"code_sha256":"50fe37ab9e685bd6"}},{"arxiv_id":"2205.10106","paper":"/paper/lense-learning-to-navigate-subgraph","title":"LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation","date":"2022-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"davidireland3/lense","path":"IM/environment.py","file_url":"https://github.com/davidireland3/lense/blob/HEAD/IM/environment.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d43be97e1132fc8f","mcp_get_code":{"code_sha256":"d43be97e1132fc8f"}},{"arxiv_id":"2205.09248","paper":"/paper/mesh2ir-neural-acoustic-impulse-response","title":"MESH2IR: Neural Acoustic Impulse Response Generator for Complex 3D Scenes","date":"2022-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anton-jeran/MESH2IR","path":"evaluate/evaluate.py","file_url":"https://github.com/anton-jeran/MESH2IR/blob/HEAD/evaluate/evaluate.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"262ee1b006585522","mcp_get_code":{"code_sha256":"262ee1b006585522"}},{"arxiv_id":"2010.08561","paper":"/paper/differentiable-quantum-architecture-search","title":"Differentiable Quantum Architecture Search","date":null,"month_inferred_from_arxiv_id":"2020-10","title_source":"archive","repo":"refraction-ray/tensorcircuit","path":"tensorcircuit/applications/graphdata.py","file_url":"https://github.com/refraction-ray/tensorcircuit/blob/HEAD/tensorcircuit/applications/graphdata.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"81055d3d808e6632","mcp_get_code":{"code_sha256":"81055d3d808e6632"}},{"arxiv_id":"1910.00760","paper":"/paper/efficient-graph-generation-with-graph","title":"Efficient Graph Generation with Graph Recurrent Attention Networks","date":"2019-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lrjconan/GRAN","path":"runner/gran_runner.py","file_url":"https://github.com/lrjconan/GRAN/blob/HEAD/runner/gran_runner.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9851241198ed98d8","mcp_get_code":{"code_sha256":"9851241198ed98d8"}},{"arxiv_id":"1907.04347","paper":"/paper/cross-domain-generalization-of-neural","title":"Cross-Domain Generalization of Neural Constituency Parsers","date":"2019-07-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"4289934e599d98d1","mcp_get_code":{"code_sha256":"4289934e599d98d1"}},{"arxiv_id":"1812.11760","paper":"/paper/multilingual-constituency-parsing-with-self","title":"Multilingual Constituency Parsing with Self-Attention and Pre-Training","date":"2018-12-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"4289934e599d98d1","mcp_get_code":{"code_sha256":"4289934e599d98d1"}},{"arxiv_id":"1704.04861","paper":"/paper/mobilenets-efficient-convolutional-neural","title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","date":"2017-04-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"allenai/dnw","path":"models/graphs/mobilenetv1like.py","file_url":"https://github.com/allenai/dnw/blob/HEAD/models/graphs/mobilenetv1like.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"821dd24d968031fe","mcp_get_code":{"code_sha256":"821dd24d968031fe"}},{"arxiv_id":"1602.07776","paper":"/paper/recurrent-neural-network-grammars","title":"Recurrent Neural Network Grammars","date":"2016-02-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dpfried/rnng-bert","path":"scripts/bert_parse.py","file_url":"https://github.com/dpfried/rnng-bert/blob/HEAD/scripts/bert_parse.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4289934e599d98d1","mcp_get_code":{"code_sha256":"4289934e599d98d1"}},{"arxiv_id":"1602.03606","paper":"/paper/variations-of-the-similarity-function-of","title":"Variations of the Similarity Function of TextRank for Automated Summarization","date":"2016-02-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"summanlp/textrank","path":"summa/summarizer.py","file_url":"https://github.com/summanlp/textrank/blob/HEAD/summa/summarizer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"72eccc45566033ed","mcp_get_code":{"code_sha256":"72eccc45566033ed"}},{"arxiv_id":"1602.03606","paper":"/paper/variations-of-the-similarity-function-of","title":"Variations of the Similarity Function of TextRank for Automated Summarization","date":"2016-02-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"summanlp/textrank","path":"summa/keywords.py","file_url":"https://github.com/summanlp/textrank/blob/HEAD/summa/keywords.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f7e39c6c068afc28","mcp_get_code":{"code_sha256":"f7e39c6c068afc28"}}]}