{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001405164,0.001172176,0.001155905,0.003811241,0.0007423706,0.001662773,0.002237364,0.001166155,0.001514256],"category_scores_gemma":[0.01133447,0.0005660016,0.0007002532,0.003131503,0.0006850019,0.004181626,0.001923742,0.001247384,0.001147358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006814075,"about_ca_system_score_gemma":0.001017769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003480492,"about_ca_topic_score_gemma":0.004504547,"domain_scores_codex":[0.9984546,0.0002889752,0.0001535561,0.0004995339,0.0004869518,0.0001164707],"domain_scores_gemma":[0.9935875,0.002751969,0.0008221985,0.001469059,0.001004708,0.0003645809],"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":[0.001553286,0.0004313385,0.04573986,0.0007269201,0.000517974,0.0005919473,0.0008240683,0.1868727,0.02392731,0.01520144,0.02012858,0.7034844],"study_design_scores_gemma":[0.00004192594,0.0001171348,0.001664016,0.00001817845,0.0000294735,0.0002769245,0.00008779213,0.969743,0.007299219,0.01728931,0.003402274,0.0000308003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09712116,0.001187267,0.8760233,0.0004424505,0.0002087753,0.000293303,0.002573253,0.02098234,0.001168169],"genre_scores_gemma":[0.5260123,0.0005297448,0.46264,0.0002240464,0.000118011,0.000264914,0.00691523,0.0007607097,0.002535012],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003811241,"threshold_uncertainty_score":0.007431328,"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."}}