{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:FERTZIGUISXGQXIIAR6UFH37LL","short_pith_number":"pith:FERTZIGU","schema_version":"1.0","canonical_sha256":"29233ca0d444ae685d08047d429f7f5af39d1e183a79ec9832c1c4362a5320bd","source":{"kind":"arxiv","id":"2102.09844","version":3},"attestation_state":"computed","paper":{"title":"E(n) Equivariant Graph Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Emiel Hoogeboom, Max Welling, Victor Garcia Satorras","submitted_at":"2021-02-19T10:25:33Z","abstract_excerpt":"This paper introduces a new model to learn graph neural networks equivariant to rotations, translations, reflections and permutations called E(n)-Equivariant Graph Neural Networks (EGNNs). In contrast with existing methods, our work does not require computationally expensive higher-order representations in intermediate layers while it still achieves competitive or better performance. In addition, whereas existing methods are limited to equivariance on 3 dimensional spaces, our model is easily scaled to higher-dimensional spaces. We demonstrate the effectiveness of our method on dynamical syste"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2102.09844","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-02-19T10:25:33Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"19f384f266bf4bab684277b2d1ed7d8aa446c306d93b2aa051073cbb61aa580c","abstract_canon_sha256":"282541c73e8fc08f057b5d006a0e085a40cc73ea6b83b73713f9b7fa9d1637ec"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:57:25.891119Z","signature_b64":"HM0b3zyN4nDy94NfxcCML1vZ8wibi1ji8z9YsTnuaKuBIZrzM21b3lIed6NkBD5/shqiydpBtkUDYwI67j5dAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"29233ca0d444ae685d08047d429f7f5af39d1e183a79ec9832c1c4362a5320bd","last_reissued_at":"2026-07-05T03:57:25.890669Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:57:25.890669Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"E(n) Equivariant Graph Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Emiel Hoogeboom, Max Welling, Victor Garcia Satorras","submitted_at":"2021-02-19T10:25:33Z","abstract_excerpt":"This paper introduces a new model to learn graph neural networks equivariant to rotations, translations, reflections and permutations called E(n)-Equivariant Graph Neural Networks (EGNNs). In contrast with existing methods, our work does not require computationally expensive higher-order representations in intermediate layers while it still achieves competitive or better performance. In addition, whereas existing methods are limited to equivariance on 3 dimensional spaces, our model is easily scaled to higher-dimensional spaces. We demonstrate the effectiveness of our method on dynamical syste"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.09844","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2102.09844/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2102.09844","created_at":"2026-07-05T03:57:25.890724+00:00"},{"alias_kind":"arxiv_version","alias_value":"2102.09844v3","created_at":"2026-07-05T03:57:25.890724+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.09844","created_at":"2026-07-05T03:57:25.890724+00:00"},{"alias_kind":"pith_short_12","alias_value":"FERTZIGUISXG","created_at":"2026-07-05T03:57:25.890724+00:00"},{"alias_kind":"pith_short_16","alias_value":"FERTZIGUISXGQXII","created_at":"2026-07-05T03:57:25.890724+00:00"},{"alias_kind":"pith_short_8","alias_value":"FERTZIGU","created_at":"2026-07-05T03:57:25.890724+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":15,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.19133","citing_title":"Equivariant Graph Neural Networks Improve Optical Spectra Prediction for Materials Screening","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2606.09480","citing_title":"Loss-Guided Adaptive Scale Refinement for Molecular Force Prediction","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2606.09432","citing_title":"Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems","ref_index":36,"is_internal_anchor":false},{"citing_arxiv_id":"2605.31498","citing_title":"Scalable Inference-Time Annealing with Surrogate Likelihood Estimators","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2606.27864","citing_title":"A Unified Framework for Vision Transformers Equivariant to Discrete Subgroups of $\\mathrm{O}(2)$","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2606.27864","citing_title":"A Unified Framework for Vision Transformers Equivariant to Discrete Subgroups of $\\mathrm{O}(2)$","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2606.20906","citing_title":"MMGNN: Multi-level, multi-color graph neural networks for molecular property prediction","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2605.22698","citing_title":"Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2510.03046","citing_title":"Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing","ref_index":61,"is_internal_anchor":false},{"citing_arxiv_id":"2511.20429","citing_title":"Estimating the triaxiality of massive clusters from 2D observables in MillenniumTNG with machine learning","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2603.10093","citing_title":"Equivariant Asynchronous Diffusion: An Adaptive Denoising Schedule for Accelerated Molecular Conformation Generation","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2104.13478","citing_title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","ref_index":77,"is_internal_anchor":false},{"citing_arxiv_id":"2604.26593","citing_title":"PiGGO: Physics-Guided Learnable Graph Kalman Filters for Virtual Sensing of Nonlinear Dynamic Structures under Uncertainty","ref_index":66,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08189","citing_title":"Equivariant Efficient Joint Discrete and Continuous MeanFlow for Molecular Graph Generation","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2605.04138","citing_title":"Galactic Amnesia: The Information Washout of the Milky Way Merger History","ref_index":157,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FERTZIGUISXGQXIIAR6UFH37LL","json":"https://pith.science/pith/FERTZIGUISXGQXIIAR6UFH37LL.json","graph_json":"https://pith.science/api/pith-number/FERTZIGUISXGQXIIAR6UFH37LL/graph.json","events_json":"https://pith.science/api/pith-number/FERTZIGUISXGQXIIAR6UFH37LL/events.json","paper":"https://pith.science/paper/FERTZIGU"},"agent_actions":{"view_html":"https://pith.science/pith/FERTZIGUISXGQXIIAR6UFH37LL","download_json":"https://pith.science/pith/FERTZIGUISXGQXIIAR6UFH37LL.json","view_paper":"https://pith.science/paper/FERTZIGU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2102.09844&json=true","fetch_graph":"https://pith.science/api/pith-number/FERTZIGUISXGQXIIAR6UFH37LL/graph.json","fetch_events":"https://pith.science/api/pith-number/FERTZIGUISXGQXIIAR6UFH37LL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FERTZIGUISXGQXIIAR6UFH37LL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FERTZIGUISXGQXIIAR6UFH37LL/action/storage_attestation","attest_author":"https://pith.science/pith/FERTZIGUISXGQXIIAR6UFH37LL/action/author_attestation","sign_citation":"https://pith.science/pith/FERTZIGUISXGQXIIAR6UFH37LL/action/citation_signature","submit_replication":"https://pith.science/pith/FERTZIGUISXGQXIIAR6UFH37LL/action/replication_record"}},"created_at":"2026-07-05T03:57:25.890724+00:00","updated_at":"2026-07-05T03:57:25.890724+00:00"}