{"id":"W2975795857","doi":"10.3389/fdata.2019.00006","title":"Attending Over Triads for Learning Signed Network Embedding","year":2019,"lang":"en","type":"article","venue":"Frontiers in Big Data","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Embedding; Leverage (statistics); Sign (mathematics); Enhanced Data Rates for GSM Evolution; Computer science; Node (physics); Theoretical computer science; Network topology; Topology (electrical circuits); Mathematics; Artificial intelligence; Combinatorics; Computer network; Engineering","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":[0.001728671,0.002034752,0.001630461,0.002242252,0.0009145762,0.001595133,0.002259088,0.002470478,0.006187991],"category_scores_gemma":[0.010437,0.0006813961,0.001371582,0.002374302,0.00127214,0.005261247,0.002514053,0.003657222,0.001513791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001370778,"about_ca_system_score_gemma":0.0008300996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003171394,"about_ca_topic_score_gemma":0.004878748,"domain_scores_codex":[0.9988766,0.000465351,0.00005611365,0.0003483437,0.0001561653,0.00009744923],"domain_scores_gemma":[0.9950788,0.003186013,0.0005287093,0.0005270641,0.000407895,0.0002715095],"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":[0.0003690463,0.0002808239,0.002749859,0.0003996374,0.0001562378,0.0002244807,0.0003432608,0.6188062,0.003377414,0.08268716,0.01459402,0.2760118],"study_design_scores_gemma":[0.000009800307,0.00003406563,0.00009724851,0.0000113524,0.00000822448,0.00002215596,0.00001937888,0.9568835,0.0003495017,0.04187744,0.000678895,0.000008511243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02012705,0.0005003038,0.9758825,0.0005349029,0.0001026364,0.00008227249,0.0003350244,0.0008418318,0.001593475],"genre_scores_gemma":[0.7281349,0.001117758,0.2534215,0.0008950349,0.0005841776,0.0005520848,0.003809447,0.0004522622,0.01103291],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006187991,"threshold_uncertainty_score":0.02070093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04617225981116451,"score_gpt":0.2949449555788986,"score_spread":0.2487726957677341,"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."}}