{"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/is-split","entry":"is_split","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":17,"n_papers_ran":0,"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":2,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":17,"n_places_pointer_only":6,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":2},"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":"2510.21267","paper":"/paper/arxiv-2510-21267","title":"Relieving the Over-Aggregating Effect in Graph Transformers","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"sunjss/over-aggregating","path":"graphgps/agg_runs.py","file_url":"https://github.com/sunjss/over-aggregating/blob/HEAD/graphgps/agg_runs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"438635c08adfe2aa","mcp_get_code":{"code_sha256":"438635c08adfe2aa"}},{"arxiv_id":"2502.09263","paper":"/paper/unlocking-the-potential-of-classic-gnns-for","title":"Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence","date":"2025-02-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LUOyk1999/GNNPlus","path":"GNNPlus/agg_runs.py","file_url":"https://github.com/LUOyk1999/GNNPlus/blob/HEAD/GNNPlus/agg_runs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"438635c08adfe2aa","mcp_get_code":{"code_sha256":"438635c08adfe2aa"}},{"arxiv_id":"2411.02059","paper":"/paper/tablegpt2-a-large-multimodal-model-with","title":"TableGPT2: A Large Multimodal Model with Tabular Data Integration","date":"2024-11-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tablegpt/tablegpt-agent","path":"src/tablegpt/agent/file_reading/data_normalizer.py","file_url":"https://github.com/tablegpt/tablegpt-agent/blob/HEAD/src/tablegpt/agent/file_reading/data_normalizer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"9b024751e4c0cdbe","mcp_get_code":{"code_sha256":"9b024751e4c0cdbe"}},{"arxiv_id":"2406.15852","paper":"/paper/next-level-message-passing-with-hierarchical","title":"Next Level Message-Passing with Hierarchical Support Graphs","date":"2024-06-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"carlosinator/support-graphs","path":"graphgps/agg_runs.py","file_url":"https://github.com/carlosinator/support-graphs/blob/HEAD/graphgps/agg_runs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"438635c08adfe2aa","mcp_get_code":{"code_sha256":"438635c08adfe2aa"}},{"arxiv_id":"2406.05815","paper":"/paper/what-can-we-learn-from-state-space-models-for","title":"What Can We Learn from State Space Models for Machine Learning on Graphs?","date":"2024-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"graph-com/gssc","path":"gssc/agg_runs.py","file_url":"https://github.com/graph-com/gssc/blob/HEAD/gssc/agg_runs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"438635c08adfe2aa","mcp_get_code":{"code_sha256":"438635c08adfe2aa"}},{"arxiv_id":"2405.21061","paper":"/paper/graph-external-attention-enhanced-transformer","title":"Graph External Attention Enhanced Transformer","date":"2024-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"icm1018/GEAET","path":"GEAET/agg_runs.py","file_url":"https://github.com/icm1018/GEAET/blob/HEAD/GEAET/agg_runs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"438635c08adfe2aa","mcp_get_code":{"code_sha256":"438635c08adfe2aa"}},{"arxiv_id":"2405.20543","paper":"/paper/towards-a-general-gnn-framework-for","title":"Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNs","date":"2024-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wenkelf/copt","path":"graphgym/agg_runs.py","file_url":"https://github.com/wenkelf/copt/blob/HEAD/graphgym/agg_runs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"438635c08adfe2aa","mcp_get_code":{"code_sha256":"438635c08adfe2aa"}},{"arxiv_id":"2405.13806","paper":"/paper/advancing-graph-convolutional-networks-via","title":"A General Graph Spectral Wavelet Convolution via Chebyshev Order Decomposition","date":"2024-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liun-online/WaveGC","path":"WaveGC_graph/graphgps/agg_runs.py","file_url":"https://github.com/liun-online/WaveGC/blob/HEAD/WaveGC_graph/graphgps/agg_runs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"438635c08adfe2aa","mcp_get_code":{"code_sha256":"438635c08adfe2aa"}},{"arxiv_id":"2405.11951","paper":"/paper/distinguished-in-uniform-self-attention-vs","title":"Distinguished In Uniform: Self Attention Vs. Virtual Nodes","date":"2024-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"toenshoff/vn-vs-gt","path":"graphgps/agg_runs.py","file_url":"https://github.com/toenshoff/vn-vs-gt/blob/HEAD/graphgps/agg_runs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"438635c08adfe2aa","mcp_get_code":{"code_sha256":"438635c08adfe2aa"}},{"arxiv_id":"2404.03380","paper":"/paper/on-the-theoretical-expressive-power-and-the","title":"On the Theoretical Expressive Power and the Design Space of Higher-Order Graph Transformers","date":"2024-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhouc20/k-transformer","path":"graphgps/agg_runs.py","file_url":"https://github.com/zhouc20/k-transformer/blob/HEAD/graphgps/agg_runs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"438635c08adfe2aa","mcp_get_code":{"code_sha256":"438635c08adfe2aa"}},{"arxiv_id":"2402.14202","paper":"/paper/comparing-graph-transformers-via-positional","title":"Comparing Graph Transformers via Positional Encodings","date":"2024-02-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"blackmit/comparing_graph_transformers_via_positional_encodings","path":"graphgps/agg_runs.py","file_url":"https://github.com/blackmit/comparing_graph_transformers_via_positional_encodings/blob/HEAD/graphgps/agg_runs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"438635c08adfe2aa","mcp_get_code":{"code_sha256":"438635c08adfe2aa"}},{"arxiv_id":"2402.02518","paper":"/paper/latent-graph-diffusion-a-unified-framework","title":"Unifying Generation and Prediction on Graphs with Latent Graph Diffusion","date":"2024-02-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhouc20/latentgraphdiffusion","path":"lgd/agg_runs.py","file_url":"https://github.com/zhouc20/latentgraphdiffusion/blob/HEAD/lgd/agg_runs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"438635c08adfe2aa","mcp_get_code":{"code_sha256":"438635c08adfe2aa"}},{"arxiv_id":"2402.02005","paper":"/paper/topology-informed-graph-transformer","title":"Topology-Informed Graph Transformer","date":"2024-02-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leemingo/tigt","path":"graphgps/agg_runs.py","file_url":"https://github.com/leemingo/tigt/blob/HEAD/graphgps/agg_runs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"438635c08adfe2aa","mcp_get_code":{"code_sha256":"438635c08adfe2aa"}},{"arxiv_id":"2312.04234","paper":"/paper/graph-convolutions-enrich-the-self-attention","title":"Graph Convolutions Enrich the Self-Attention in Transformers!","date":"2023-12-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rampasek/GraphGPS","path":"graphgps/agg_runs.py","file_url":"https://github.com/rampasek/GraphGPS/blob/HEAD/graphgps/agg_runs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"438635c08adfe2aa","mcp_get_code":{"code_sha256":"438635c08adfe2aa"}},{"arxiv_id":"2310.19285","paper":"/paper/facilitating-graph-neural-networks-with","title":"Facilitating Graph Neural Networks with Random Walk on Simplicial Complexes","date":"2023-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhouc20/HodgeRandomWalk","path":"graphgps/agg_runs.py","file_url":"https://github.com/zhouc20/HodgeRandomWalk/blob/HEAD/graphgps/agg_runs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"438635c08adfe2aa","mcp_get_code":{"code_sha256":"438635c08adfe2aa"}},{"arxiv_id":"2309.00367","paper":"/paper/where-did-the-gap-go-reassessing-the-long","title":"Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark","date":"2023-09-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"toenshoff/lrgb","path":"graphgps/agg_runs.py","file_url":"https://github.com/toenshoff/lrgb/blob/HEAD/graphgps/agg_runs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"438635c08adfe2aa","mcp_get_code":{"code_sha256":"438635c08adfe2aa"}},{"arxiv_id":"2302.04181","paper":"/paper/attending-to-graph-transformers","title":"Attending to Graph Transformers","date":"2023-02-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"luis-mueller/probing-graph-transformers","path":"graphgps/agg_runs.py","file_url":"https://github.com/luis-mueller/probing-graph-transformers/blob/HEAD/graphgps/agg_runs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"438635c08adfe2aa","mcp_get_code":{"code_sha256":"438635c08adfe2aa"}}]}