{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:MKOUJY4I6RU3DPUDA4KAJWXEIY","short_pith_number":"pith:MKOUJY4I","schema_version":"1.0","canonical_sha256":"629d44e388f469b1be83071404dae44628689a92ad4381bf937a204f9cfcd494","source":{"kind":"arxiv","id":"2004.11198","version":3},"attestation_state":"computed","paper":{"title":"SIGN: Scalable Inception Graph Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ben Chamberlain, Davide Eynard, Emanuele Rossi, Fabrizio Frasca, Federico Monti, Michael Bronstein","submitted_at":"2020-04-23T14:46:10Z","abstract_excerpt":"Graph representation learning has recently been applied to a broad spectrum of problems ranging from computer graphics and chemistry to high energy physics and social media. The popularity of graph neural networks has sparked interest, both in academia and in industry, in developing methods that scale to very large graphs such as Facebook or Twitter social networks. In most of these approaches, the computational cost is alleviated by a sampling strategy retaining a subset of node neighbors or subgraphs at training time. In this paper we propose a new, efficient and scalable graph deep learning"},"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":"2004.11198","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-04-23T14:46:10Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"9810d4a3f840b668932645e9478072d5a6b95879cb82ae2c80d6e8d103a0b127","abstract_canon_sha256":"86207042fae4a0b01f7d2928cd29573108ecacae4c97bb9a2457fc5afd2210a6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:49:02.775829Z","signature_b64":"6quw2sSjiOU21ycMGdESM2aAntc6/WzQZNH3PudGG00a6ttiN5hB3fX2z6EIqTSaAbqpXRqlrHKqEa1bL8P+Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"629d44e388f469b1be83071404dae44628689a92ad4381bf937a204f9cfcd494","last_reissued_at":"2026-07-05T01:49:02.775336Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:49:02.775336Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SIGN: Scalable Inception Graph Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ben Chamberlain, Davide Eynard, Emanuele Rossi, Fabrizio Frasca, Federico Monti, Michael Bronstein","submitted_at":"2020-04-23T14:46:10Z","abstract_excerpt":"Graph representation learning has recently been applied to a broad spectrum of problems ranging from computer graphics and chemistry to high energy physics and social media. The popularity of graph neural networks has sparked interest, both in academia and in industry, in developing methods that scale to very large graphs such as Facebook or Twitter social networks. In most of these approaches, the computational cost is alleviated by a sampling strategy retaining a subset of node neighbors or subgraphs at training time. In this paper we propose a new, efficient and scalable graph deep learning"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.11198","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/2004.11198/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":"2004.11198","created_at":"2026-07-05T01:49:02.775414+00:00"},{"alias_kind":"arxiv_version","alias_value":"2004.11198v3","created_at":"2026-07-05T01:49:02.775414+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.11198","created_at":"2026-07-05T01:49:02.775414+00:00"},{"alias_kind":"pith_short_12","alias_value":"MKOUJY4I6RU3","created_at":"2026-07-05T01:49:02.775414+00:00"},{"alias_kind":"pith_short_16","alias_value":"MKOUJY4I6RU3DPUD","created_at":"2026-07-05T01:49:02.775414+00:00"},{"alias_kind":"pith_short_8","alias_value":"MKOUJY4I","created_at":"2026-07-05T01:49:02.775414+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":11,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.26664","citing_title":"TGHE: Template-based Graph Homomorphic Encryption for Privacy-Preserving GNN Inference in Edge-Cloud Systems","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2606.22429","citing_title":"Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation","ref_index":131,"is_internal_anchor":false},{"citing_arxiv_id":"2606.01660","citing_title":"Gate the Filter, Not the Message: Node-Channel Mixtures for Pre-Propagation GNNs","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2606.32016","citing_title":"FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning","ref_index":116,"is_internal_anchor":false},{"citing_arxiv_id":"2605.25111","citing_title":"Revisiting Pre-Propagation GNNs: Robust Diffusion Operators and Hidden-State Re-Propagation","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2006.10637","citing_title":"Temporal Graph Networks for Deep Learning on Dynamic Graphs","ref_index":140,"is_internal_anchor":false},{"citing_arxiv_id":"2602.17071","citing_title":"AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagation","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10975","citing_title":"Hierarchical Multi-Scale Graph Neural Networks: Scalable Heterophilous Learning with Oversmoothing and Oversquashing Mitigation","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2605.11468","citing_title":"CAMPA: Efficient and Aligned Multimodal Graph Learning via Decoupled Propagation and Aggregation","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2604.22676","citing_title":"Operational Feature Fingerprints of Graph Datasets via a White-Box Signal-Subspace Probe","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01484","citing_title":"Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks","ref_index":166,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MKOUJY4I6RU3DPUDA4KAJWXEIY","json":"https://pith.science/pith/MKOUJY4I6RU3DPUDA4KAJWXEIY.json","graph_json":"https://pith.science/api/pith-number/MKOUJY4I6RU3DPUDA4KAJWXEIY/graph.json","events_json":"https://pith.science/api/pith-number/MKOUJY4I6RU3DPUDA4KAJWXEIY/events.json","paper":"https://pith.science/paper/MKOUJY4I"},"agent_actions":{"view_html":"https://pith.science/pith/MKOUJY4I6RU3DPUDA4KAJWXEIY","download_json":"https://pith.science/pith/MKOUJY4I6RU3DPUDA4KAJWXEIY.json","view_paper":"https://pith.science/paper/MKOUJY4I","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2004.11198&json=true","fetch_graph":"https://pith.science/api/pith-number/MKOUJY4I6RU3DPUDA4KAJWXEIY/graph.json","fetch_events":"https://pith.science/api/pith-number/MKOUJY4I6RU3DPUDA4KAJWXEIY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MKOUJY4I6RU3DPUDA4KAJWXEIY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MKOUJY4I6RU3DPUDA4KAJWXEIY/action/storage_attestation","attest_author":"https://pith.science/pith/MKOUJY4I6RU3DPUDA4KAJWXEIY/action/author_attestation","sign_citation":"https://pith.science/pith/MKOUJY4I6RU3DPUDA4KAJWXEIY/action/citation_signature","submit_replication":"https://pith.science/pith/MKOUJY4I6RU3DPUDA4KAJWXEIY/action/replication_record"}},"created_at":"2026-07-05T01:49:02.775414+00:00","updated_at":"2026-07-05T01:49:02.775414+00:00"}