{"id":"W2235825999","doi":"10.1007/978-3-662-44415-3_9","title":"A Hausdorff Heuristic for Efficient Computation of Graph Edit Distance","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Graph Theory and Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Hasler Stiftung; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Edit distance; Hausdorff distance; Computer science; Computation; Heuristic; Matching (statistics); Algorithm; Graph; Theoretical computer science; Computational complexity theory; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001749231,0.00138528,0.002568476,0.004165649,0.001259971,0.003334707,0.00372866,0.001800992,0.009517463],"category_scores_gemma":[0.01205703,0.0008508895,0.001694398,0.004441884,0.001359602,0.0050092,0.002571314,0.002470155,0.002850656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001947363,"about_ca_system_score_gemma":0.00195092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00350582,"about_ca_topic_score_gemma":0.005215921,"domain_scores_codex":[0.9978374,0.0005597409,0.0002056197,0.0005220664,0.0006805902,0.000194548],"domain_scores_gemma":[0.9935172,0.003465358,0.0002484905,0.001748246,0.0007452521,0.0002755661],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006452005,0.0003677458,0.001509253,0.0005726873,0.0002662498,0.0003209332,0.0004173161,0.168599,0.01384043,0.1480876,0.02195899,0.6434147],"study_design_scores_gemma":[0.00008067318,0.0001366852,0.0005915484,0.00004112029,0.00007776969,0.0002180791,0.0001177168,0.8383031,0.007339615,0.1465604,0.006462388,0.00007080592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01706234,0.0006384985,0.9756775,0.0001528932,0.0001821163,0.0001147842,0.000392576,0.00298445,0.002794825],"genre_scores_gemma":[0.1364007,0.000298188,0.8575637,0.0001189036,0.0001373266,0.0002194865,0.001531609,0.0007896848,0.002940286],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009517463,"threshold_uncertainty_score":0.03183907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009986013772851765,"score_gpt":0.2334601779373866,"score_spread":0.2234741641645348,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}