{"id":"W4240758720","doi":"10.32920/14668887","title":"Dimensionality of Social Networks Using Motifs and Eigenvalues","year":2021,"lang":"en","type":"article","venue":"","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; National Science Foundation","keywords":"Curse of dimensionality; Eigenvalues and eigenvectors; Dimension (graph theory); Exploit; Logarithm; Metric (unit); Intrinsic dimension; Computer science; Complex network; Social network (sociolinguistics); Space (punctuation); Distribution (mathematics); Theoretical computer science; Mathematics; Artificial intelligence; Social media; Combinatorics; Physics; Engineering; Computer security; World Wide Web","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":[0.002680421,0.0006330265,0.0005961535,0.001926628,0.0007022805,0.00199359,0.0005757352,0.0007827053,0.001050869],"category_scores_gemma":[0.03522533,0.0003917726,0.0006636078,0.001410056,0.001369138,0.004462244,0.001223013,0.001101562,0.0002102921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001191935,"about_ca_system_score_gemma":0.0004981986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002080029,"about_ca_topic_score_gemma":0.001939987,"domain_scores_codex":[0.9983005,0.0008550458,0.00008870806,0.000340792,0.0003254038,0.00008963302],"domain_scores_gemma":[0.9709352,0.02146976,0.003224441,0.002719686,0.001055646,0.0005954037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007903526,0.0006906252,0.2038435,0.0006215057,0.0005516353,0.0003343333,0.002624969,0.5204422,0.01684669,0.1414078,0.00424885,0.1075974],"study_design_scores_gemma":[0.0000231319,0.0001234385,0.01572362,0.00003527298,0.00003263755,0.0001941184,0.000254757,0.8925161,0.002058982,0.08812322,0.0008589944,0.00005572112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7931405,0.0005946789,0.1998113,0.001122562,0.00004293443,0.0001457886,0.000834869,0.0004407081,0.003866718],"genre_scores_gemma":[0.9732999,0.0001544008,0.02593929,0.00004163964,0.00002690383,0.00006161063,0.0002256182,0.00002447406,0.000226157],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002680421,"threshold_uncertainty_score":0.01417559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01920140779560532,"score_gpt":0.2892014757661063,"score_spread":0.270000067970501,"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."}}