{"id":"W4319988572","doi":"10.1007/978-3-031-21131-7_22","title":"Winner Does Not Take All: Contrasting Centrality in Adversarial Networks","year":2023,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Adversarial system; Centrality; PageRank; Key (lock); Computer science; Dominance (genetics); Theoretical computer science; Node (physics); Graph; Network science; Complex network; Artificial intelligence; Computer security; Mathematics; Combinatorics; Engineering; World Wide Web","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006108141,0.0004553857,0.0008097214,0.0002564408,0.000114165,0.00004364049,0.0003710436,0.0001280763,0.0002264117],"category_scores_gemma":[0.00004653252,0.0004176677,0.0002470475,0.0001873082,0.0003379568,0.00008153007,0.000443295,0.0007448002,0.00003573322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002189299,"about_ca_system_score_gemma":0.00006816268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002187099,"about_ca_topic_score_gemma":0.0004950779,"domain_scores_codex":[0.9972804,0.0000666101,0.001062579,0.0006846345,0.000402308,0.0005034924],"domain_scores_gemma":[0.9974498,0.001622221,0.0003830996,0.0002350335,0.0002552351,0.00005456916],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003912796,0.00003801064,0.00286795,0.00002821677,0.0006146382,0.00003116095,0.0004471994,0.4806325,3.660762e-7,0.4958953,0.0009551215,0.01845033],"study_design_scores_gemma":[0.0001880621,0.00002983508,0.0007906336,0.0007757481,0.00009790053,6.877975e-7,0.000441155,0.175888,0.00001203557,0.8181039,0.002962764,0.0007092264],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001181923,0.00229404,0.8092176,0.001636466,0.006087948,0.002951479,0.0006169624,0.0007630797,0.1752505],"genre_scores_gemma":[0.9786097,0.0002813806,0.002711535,0.0001585456,0.001449316,0.0000710415,0.0003503115,0.00007638166,0.01629183],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9774277,"threshold_uncertainty_score":0.9998275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.108208395498178,"score_gpt":0.3690246951033868,"score_spread":0.2608162996052089,"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."}}