{"id":"W4285605397","doi":"10.24963/ijcai.2022/296","title":"HashNWalk: Hash and Random Walk Based Anomaly Detection in Hyperedge Streams","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Korea Advanced Institute of Science and Technology; National Research Foundation of Korea; National Research Foundation","keywords":"Anomaly detection; Computer science; Hash function; Random walk; Constant (computer programming); Data stream; Anomaly (physics); Graph; Theoretical computer science; Data mining; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006001177,0.0002124657,0.0002943258,0.0002912976,0.0003247825,0.000131921,0.0006645803,0.00003383154,0.0007453682],"category_scores_gemma":[0.0000619089,0.0001860677,0.0001667613,0.0004258922,0.0001603567,0.0001562032,0.0004123043,0.000436586,0.000006164345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000125873,"about_ca_system_score_gemma":0.00004527215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006699635,"about_ca_topic_score_gemma":0.0001726535,"domain_scores_codex":[0.9982013,0.00003297957,0.0006048044,0.0004095713,0.0005036268,0.0002477336],"domain_scores_gemma":[0.9989831,0.0001019731,0.0004144883,0.0001502334,0.0002981681,0.00005199966],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001819038,0.001609646,0.05973791,0.00006270224,0.0002236546,0.000002165919,0.001766151,0.005099672,0.06422441,0.5574586,0.0003620138,0.307634],"study_design_scores_gemma":[0.0003665304,0.0003455979,0.004943741,0.0002085784,0.0000560497,0.000004494056,0.002178409,0.4203719,0.3875403,0.1825629,0.0009600883,0.0004613166],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9786286,0.00001414069,0.007041382,0.003500147,0.0004217232,0.0005469521,0.00004347233,0.00006263133,0.009740941],"genre_scores_gemma":[0.999175,0.000004943229,0.0003380307,0.000102579,0.0001122339,0.0001457581,0.000007186738,0.00001622005,0.00009801125],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4152723,"threshold_uncertainty_score":0.8161259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03953152171979173,"score_gpt":0.2652976275254189,"score_spread":0.2257661058056271,"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."}}