{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/predict-link-probabilities","entry":"predict_link_probabilities","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":6,"n_papers_ran":6,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":1,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":6,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":0},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2506.08309","paper":"/paper/learnable-spatial-temporal-positional","title":"Learnable Spatial-Temporal Positional Encoding for Link Prediction","date":"2025-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kthrn22/l-step","path":"models/EdgeBank.py","file_url":"https://github.com/kthrn22/l-step/blob/HEAD/models/EdgeBank.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8bcef3dc22f0489a","mcp_get_code":{"code_sha256":"8bcef3dc22f0489a"}},{"arxiv_id":"2410.04013","paper":"/paper/improving-temporal-link-prediction-via","title":"Improving Temporal Link Prediction via Temporal Walk Matrix Projection","date":"2024-10-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lxd99/TPNet","path":"models/EdgeBank.py","file_url":"https://github.com/lxd99/TPNet/blob/HEAD/models/EdgeBank.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8bcef3dc22f0489a","mcp_get_code":{"code_sha256":"8bcef3dc22f0489a"}},{"arxiv_id":"2408.06966","paper":"/paper/dyg-mamba-continuous-state-space-modeling-on","title":"DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs","date":"2024-08-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Clearloveyuan/DyG-Mamba","path":"models/EdgeBank.py","file_url":"https://github.com/Clearloveyuan/DyG-Mamba/blob/HEAD/models/EdgeBank.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8bcef3dc22f0489a","mcp_get_code":{"code_sha256":"8bcef3dc22f0489a"}},{"arxiv_id":"2405.17473","paper":"/paper/repeat-aware-neighbor-sampling-for-dynamic","title":"Repeat-Aware Neighbor Sampling for Dynamic Graph Learning","date":"2024-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Hope-Rita/RepeatMixer","path":"models/EdgeBank.py","file_url":"https://github.com/Hope-Rita/RepeatMixer/blob/HEAD/models/EdgeBank.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8bcef3dc22f0489a","mcp_get_code":{"code_sha256":"8bcef3dc22f0489a"}},{"arxiv_id":"2307.12510","paper":"/paper/an-empirical-evaluation-of-temporal-graph","title":"An Empirical Evaluation of Temporal Graph Benchmark","date":"2023-07-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yule-BUAA/DyGLib_TGB","path":"models/EdgeBank.py","file_url":"https://github.com/yule-BUAA/DyGLib_TGB/blob/HEAD/models/EdgeBank.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8bcef3dc22f0489a","mcp_get_code":{"code_sha256":"8bcef3dc22f0489a"}},{"arxiv_id":"2307.01026","paper":"/paper/temporal-graph-benchmark-for-machine-learning-1","title":"Temporal Graph Benchmark for Machine Learning on Temporal Graphs","date":"2023-07-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"timpostuvan/CTDG-link-anomaly-detection","path":"models/EdgeBank.py","file_url":"https://github.com/timpostuvan/CTDG-link-anomaly-detection/blob/HEAD/models/EdgeBank.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8bcef3dc22f0489a","mcp_get_code":{"code_sha256":"8bcef3dc22f0489a"}}]}