{"id":"W4318147794","doi":"10.1109/bigdata55660.2022.10020299","title":"Temporal Graph Representation Learning via Maximal Cliques","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Big Data (Big Data)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Clique; Computer science; Graph; Node (physics); Theoretical computer science; Embedding; Representation (politics); Graph embedding; Feature learning; Artificial intelligence; Mathematics; Combinatorics","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":[0.0007572331,0.0009151363,0.0009099173,0.002196542,0.0007691778,0.000856853,0.001792654,0.001156081,0.003151304],"category_scores_gemma":[0.004555995,0.0005481379,0.00131201,0.002474522,0.0007372856,0.00308604,0.001288154,0.001467728,0.0006382394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001273045,"about_ca_system_score_gemma":0.0009455015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009070338,"about_ca_topic_score_gemma":0.02029452,"domain_scores_codex":[0.9993224,0.0001693816,0.00002819148,0.0002695223,0.0001361195,0.00007430583],"domain_scores_gemma":[0.9982273,0.0008627985,0.0002361142,0.0003003655,0.0002588323,0.0001146229],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002769576,0.0003053334,0.007614385,0.0003610276,0.0002719382,0.0003002688,0.0003826895,0.5407816,0.005163216,0.06339067,0.018522,0.3626299],"study_design_scores_gemma":[0.00001062005,0.00001545646,0.000344723,0.00001242331,0.00001554451,0.00002955282,0.00002656566,0.9699661,0.0004170141,0.02783577,0.001320209,0.000006079487],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0626722,0.0006402356,0.9301938,0.0005754046,0.00008400137,0.0001256476,0.001098976,0.001596072,0.003013665],"genre_scores_gemma":[0.6790603,0.000725839,0.3056372,0.0004025857,0.0001749831,0.0003163344,0.006678321,0.0004133111,0.00659115],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009070338,"threshold_uncertainty_score":0.01803505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.304841879149363,"score_gpt":0.3731440942115506,"score_spread":0.06830221506218759,"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."}}