{"id":"W3098982997","doi":"","title":"Adjusting for Network Size and Composition Effects in Exponential-Family Random Graph Models.","year":2011,"lang":"en","type":"article","venue":"Research Online (University of Wollongong)","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Naval Research; Fundação para a Ciência e a Tecnologia; National Sanitarium Association; University of Washington; National Institutes of Health; National Science Foundation","keywords":"Exponential random graph models; Homophily; Random graph; Exponential family; Mathematics; Exponential function; Social network (sociolinguistics); Graph; Theoretical computer science; Econometrics; Statistics; Computer science; Discrete mathematics; Combinatorics; Social media","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008140305,0.0001188627,0.0003479741,0.0002529684,0.0002530096,0.00001382856,0.0002558722,0.00005267223,0.00003929641],"category_scores_gemma":[0.00001253121,0.0001404253,0.0001539688,0.0005991352,0.0001747225,0.0002142995,0.0002366497,0.0002508064,7.316677e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002872832,"about_ca_system_score_gemma":0.00003969111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003151215,"about_ca_topic_score_gemma":0.0003664099,"domain_scores_codex":[0.9987031,0.0002423315,0.0001517937,0.0002864043,0.000222526,0.000393819],"domain_scores_gemma":[0.998726,0.0006761992,0.00008946289,0.0002102979,0.0002001193,0.00009794847],"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":[0.01716659,0.009047717,0.2499718,0.00192046,0.003509054,0.000206222,0.02218795,0.02488277,0.05276262,0.2828433,0.01648641,0.3190151],"study_design_scores_gemma":[0.01598709,0.0009096124,0.1896359,0.001147687,0.0003518638,0.000001804819,0.006752267,0.4393293,0.000927606,0.3436632,0.000351268,0.000942433],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8762299,0.0001919679,0.1217487,0.00005740372,0.00001705567,0.0006094297,0.00002366661,0.00003249514,0.001089379],"genre_scores_gemma":[0.9655531,0.00005440222,0.03412841,0.00000449248,0.00009942058,0.000003845179,0.00006038881,0.00001236835,0.00008357524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4144465,"threshold_uncertainty_score":0.5726376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06679487743711192,"score_gpt":0.2962228445443931,"score_spread":0.2294279671072811,"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."}}