{"id":"W2950907416","doi":"10.48550/arxiv.1812.02356","title":"dynnode2vec: Scalable Dynamic Network Embedding","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Embedding; Computer science; Graph embedding; Scalability; Random walk; Theoretical computer science; Timestamp; Dynamic network analysis; Graph; Representation (politics); Vector space; Artificial intelligence; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004670736,0.002208516,0.001075816,0.001354271,0.0004748721,0.001204159,0.001863099,0.0008745819,0.003930933],"category_scores_gemma":[0.00275859,0.00068247,0.0008885975,0.002018769,0.0004395875,0.00278543,0.001454714,0.00173381,0.002586524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008380972,"about_ca_system_score_gemma":0.001081623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01292536,"about_ca_topic_score_gemma":0.02753236,"domain_scores_codex":[0.9993943,0.0001351778,0.00003356719,0.0002005468,0.0001793649,0.00005691884],"domain_scores_gemma":[0.999262,0.0002022661,0.00006137638,0.0002164872,0.0002125384,0.00004536482],"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":[0.0002488,0.0002910907,0.003594186,0.0005641234,0.0004047835,0.0003138091,0.0001829692,0.3162101,0.008860209,0.01823698,0.1922951,0.4587978],"study_design_scores_gemma":[0.00002016649,0.0000370289,0.0003620456,0.00001020191,0.00001126983,0.00006764639,0.00002757313,0.9811577,0.002468066,0.008284504,0.007534747,0.00001907008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04145361,0.001814267,0.8840588,0.001031876,0.0006521746,0.0002976241,0.0167862,0.0485654,0.00533998],"genre_scores_gemma":[0.3429873,0.001648713,0.5593029,0.0006860896,0.0002591659,0.0007945594,0.07972762,0.003278119,0.01131544],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01292536,"threshold_uncertainty_score":0.02570027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03899550368280359,"score_gpt":0.2011969557260333,"score_spread":0.1622014520432297,"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."}}