{"id":"W7147212645","doi":"10.1109/iccsm66818.2025.00011","title":"HeuGAT: Integrating Heuristic and Graph Attention Network for Improved Link Prediction and Breakup Prediction in Social Network Structures","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Attention network; Breakup; Graph; Link (geometry); Social network (sociolinguistics); Heuristic; Network structure; Network science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0008720956,0.0006362818,0.000704402,0.0003437097,0.001558709,0.0006048233,0.0003833009,0.0006028812,0.000004978562],"category_scores_gemma":[0.0001332411,0.0006360486,0.0001918784,0.002175994,0.0003752326,0.001039316,0.0004865467,0.000929927,2.716108e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001208866,"about_ca_system_score_gemma":0.00009439773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000675505,"about_ca_topic_score_gemma":0.0004719381,"domain_scores_codex":[0.9954704,0.0003091322,0.001288755,0.001544734,0.000242737,0.001144249],"domain_scores_gemma":[0.9981033,0.0006088592,0.0004749369,0.0003934513,0.0002480487,0.0001713775],"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":[0.0006736121,0.00007962824,0.06072101,0.0004717717,0.00024446,0.000005014334,0.0005449608,0.03745098,0.0004569765,0.1452184,0.009268878,0.7448643],"study_design_scores_gemma":[0.001523336,0.0003405521,0.132058,0.0003949628,0.0001011394,0.00001328008,0.00006184343,0.6355122,0.000007012434,0.2292977,0.0003900549,0.0002999475],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02462524,0.003401255,0.9625489,0.001835655,0.004761421,0.002261254,0.00006604894,0.0002875604,0.000212679],"genre_scores_gemma":[0.9340624,0.001048488,0.05942396,0.0007715548,0.003833924,0.0002293277,0.0001106108,0.00005209355,0.0004676814],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9094371,"threshold_uncertainty_score":0.9997411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008024598306539243,"score_gpt":0.2497511939309358,"score_spread":0.2417265956243966,"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."}}