{"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":"/paper/equivariant-graph-hierarchy-based-neural","title":"Equivariant Graph Hierarchy-Based Neural Networks","arxiv_id":"2202.10643","date":"2022-02-22","proceeding":null,"authors":["Jiaqi Han","Wenbing Huang","Tingyang Xu","Yu Rong"],"abstract":"Equivariant Graph neural Networks (EGNs) are powerful in characterizing the dynamics of multi-body physical systems. Existing EGNs conduct flat message passing, which, yet, is unable to capture the spatial/dynamical hierarchy for complex systems particularly, limiting substructure discovery and global information fusion. In this paper, we propose Equivariant Hierarchy-based Graph Networks (EGHNs) which consist of the three key components: generalized Equivariant Matrix Message Passing (EMMP) , E-Pool and E-UpPool. In particular, EMMP is able to improve the expressivity of conventional equivariant message passing, E-Pool assigns the quantities of the low-level nodes into high-level clusters, while E-UpPool leverages the high-level information to update the dynamics of the low-level nodes. As their names imply, both E-Pool and E-UpPool are guaranteed to be equivariant to meet physic symmetry. Considerable experimental evaluations verify the effectiveness of our EGHN on several applications including multi-object dynamics simulation, motion capture, and protein dynamics modeling.","url_abs":"https://arxiv.org/abs/2202.10643v2","url_pdf":"https://arxiv.org/pdf/2202.10643v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"equivariant-graph-hierarchy-based-neural","repo_url":"https://github.com/hanjq17/eghn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2202.10643","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.10643"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/hanjq17/eghn","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":5,"ran_fixture":1,"unverified":2},"by_repo_kind":{"official":{"samples":8,"ran":6,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"c13557c4c4b42aff","entry":"aggregate","repo":"hanjq17/eghn","repo_kind":"official","path":"model/basic.py","file_url":"https://github.com/hanjq17/eghn/blob/HEAD/model/basic.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c13557c4c4b42aff"}},{"code_sha256_prefix":"d46f2eac5def9db4","entry":"collector","repo":"hanjq17/eghn","repo_kind":"official","path":"utils.py","file_url":"https://github.com/hanjq17/eghn/blob/HEAD/utils.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"d46f2eac5def9db4"}},{"code_sha256_prefix":"09a6af5717cfe3e4","entry":"collector_simulation","repo":"hanjq17/eghn","repo_kind":"official","path":"utils.py","file_url":"https://github.com/hanjq17/eghn/blob/HEAD/utils.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"09a6af5717cfe3e4"}},{"code_sha256_prefix":"00b1bc1dee272f18","entry":"parse_amc","repo":"hanjq17/eghn","repo_kind":"official","path":"motion/amc_parser.py","file_url":"https://github.com/hanjq17/eghn/blob/HEAD/motion/amc_parser.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"00b1bc1dee272f18"}},{"code_sha256_prefix":"2cafa259d5ab3508","entry":"read_line","repo":"hanjq17/eghn","repo_kind":"official","path":"motion/amc_parser.py","file_url":"https://github.com/hanjq17/eghn/blob/HEAD/motion/amc_parser.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"2cafa259d5ab3508"}},{"code_sha256_prefix":"bc66f19be57720e7","entry":"unsorted_segment_mean","repo":"hanjq17/eghn","repo_kind":"official","path":"model/basic.py","file_url":"https://github.com/hanjq17/eghn/blob/HEAD/model/basic.py","link_basis":"harvester_set","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"bc66f19be57720e7"}},{"code_sha256_prefix":"7d820c2f22842d1c","entry":"collate_mda","repo":"hanjq17/eghn","repo_kind":"official","path":"mdanalysis/dataset.py","file_url":"https://github.com/hanjq17/eghn/blob/HEAD/mdanalysis/dataset.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"7d820c2f22842d1c"}},{"code_sha256_prefix":"559b84475945a669","entry":"do_padding","repo":"hanjq17/eghn","repo_kind":"official","path":"utils.py","file_url":"https://github.com/hanjq17/eghn/blob/HEAD/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"559b84475945a669"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}