{"id":"W2111347866","doi":"10.1109/tvcg.2007.70582","title":"NodeTrix: a Hybrid Visualization of Social Networks","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":553,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research","funders":"","keywords":"Computer science; Visualization; Representation (politics); Data visualization; Set (abstract data type); Key (lock); Social network analysis; Adjacency matrix; Node (physics); Data science; Theoretical computer science; Adjacency list; Information visualization; Graph drawing; Data mining; World Wide Web; Graph; Social media; Algorithm; Programming language","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":[0.0002671724,0.0001743014,0.0002378625,0.0003969513,0.0002615622,0.00004687,0.00009745642,0.00005983523,0.00008370995],"category_scores_gemma":[3.31343e-7,0.0001891162,0.0001553408,0.0008651665,0.00008689232,0.0001081476,0.00000304692,0.0001249163,0.000001105225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001291208,"about_ca_system_score_gemma":0.00001407377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000335199,"about_ca_topic_score_gemma":0.00001286691,"domain_scores_codex":[0.9988576,0.00006801195,0.0004195385,0.0002455209,0.0002127485,0.0001965376],"domain_scores_gemma":[0.999358,0.00008144764,0.0001792885,0.0001378221,0.0001778952,0.00006552538],"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":[0.00006186395,0.0005797622,0.002314609,0.00002213848,0.0002132767,9.279936e-7,0.0005134909,0.00259541,0.00003942327,0.9558716,0.0006672225,0.03712025],"study_design_scores_gemma":[0.0004988797,0.0001369003,0.001592499,0.00003014925,0.0001041179,0.00000111643,0.00005074159,0.9890976,0.005219771,0.002177837,0.0008202126,0.0002701791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03150703,0.00001330967,0.9678363,0.000006470406,0.0001559645,0.0001523825,0.000009625787,0.000112143,0.0002068299],"genre_scores_gemma":[0.9992326,0.00002035937,0.000277546,0.0001619415,0.0001974403,0.000007610346,0.00004634504,0.00002431265,0.00003184799],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9865022,"threshold_uncertainty_score":0.7711934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01199413693648282,"score_gpt":0.286755499340916,"score_spread":0.2747613624044332,"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."}}