{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:WTBRTO32GL6VG5ZVSZ7O7MITJP","short_pith_number":"pith:WTBRTO32","schema_version":"1.0","canonical_sha256":"b4c319bb7a32fd537735967eefb1134bcf1901231f92debf37a3594917080f7e","source":{"kind":"arxiv","id":"2003.02454","version":4},"attestation_state":"computed","paper":{"title":"AGL: a Scalable System for Industrial-purpose Graph Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SI","authors_text":"Dalong Zhang, Jun Zhou, Lin Wang, Xianzheng Song, Xin Huang, Yang Shuang, Yuan Qi, Zhibang Ge, Zhiqiang Zhang, Zhiyang Hu, Ziqi Liu","submitted_at":"2020-03-05T06:54:33Z","abstract_excerpt":"Machine learning over graphs have been emerging as powerful learning tools for graph data. However, it is challenging for industrial communities to leverage the techniques, such as graph neural networks (GNNs), and solve real-world problems at scale because of inherent data dependency in the graphs. As such, we cannot simply train a GNN with classic learning systems, for instance parameter server that assumes data parallel. Existing systems store the graph data in-memory for fast accesses either in a single machine or graph stores from remote. The major drawbacks are in three-fold. First, they"},"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":"2003.02454","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2020-03-05T06:54:33Z","cross_cats_sorted":[],"title_canon_sha256":"f40dddced679b2b83db7b5502122df7dc0fe0b53fb84676aafc4285274978949","abstract_canon_sha256":"1b9a740031729de2442846c553c3dad03c90ebe788a0523d8668675c492ff825"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:48:03.718359Z","signature_b64":"JO/SngZlbnmW6cTAeZWNVqKgmwJ07OUvkOsmidsukjDLL5z7SNjWnGnlwlpKY2Bt1cwwiMB/mmc+ggkq/cSrAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b4c319bb7a32fd537735967eefb1134bcf1901231f92debf37a3594917080f7e","last_reissued_at":"2026-07-05T00:48:03.717888Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:48:03.717888Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AGL: a Scalable System for Industrial-purpose Graph Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SI","authors_text":"Dalong Zhang, Jun Zhou, Lin Wang, Xianzheng Song, Xin Huang, Yang Shuang, Yuan Qi, Zhibang Ge, Zhiqiang Zhang, Zhiyang Hu, Ziqi Liu","submitted_at":"2020-03-05T06:54:33Z","abstract_excerpt":"Machine learning over graphs have been emerging as powerful learning tools for graph data. However, it is challenging for industrial communities to leverage the techniques, such as graph neural networks (GNNs), and solve real-world problems at scale because of inherent data dependency in the graphs. As such, we cannot simply train a GNN with classic learning systems, for instance parameter server that assumes data parallel. Existing systems store the graph data in-memory for fast accesses either in a single machine or graph stores from remote. The major drawbacks are in three-fold. First, they"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.02454","kind":"arxiv","version":4},"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/2003.02454/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":"2003.02454","created_at":"2026-07-05T00:48:03.717956+00:00"},{"alias_kind":"arxiv_version","alias_value":"2003.02454v4","created_at":"2026-07-05T00:48:03.717956+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.02454","created_at":"2026-07-05T00:48:03.717956+00:00"},{"alias_kind":"pith_short_12","alias_value":"WTBRTO32GL6V","created_at":"2026-07-05T00:48:03.717956+00:00"},{"alias_kind":"pith_short_16","alias_value":"WTBRTO32GL6VG5ZV","created_at":"2026-07-05T00:48:03.717956+00:00"},{"alias_kind":"pith_short_8","alias_value":"WTBRTO32","created_at":"2026-07-05T00:48:03.717956+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.11180","citing_title":"TINED: GNNs-to-MLPs by Teacher Injection and Dirichlet Energy Distillation","ref_index":45,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WTBRTO32GL6VG5ZVSZ7O7MITJP","json":"https://pith.science/pith/WTBRTO32GL6VG5ZVSZ7O7MITJP.json","graph_json":"https://pith.science/api/pith-number/WTBRTO32GL6VG5ZVSZ7O7MITJP/graph.json","events_json":"https://pith.science/api/pith-number/WTBRTO32GL6VG5ZVSZ7O7MITJP/events.json","paper":"https://pith.science/paper/WTBRTO32"},"agent_actions":{"view_html":"https://pith.science/pith/WTBRTO32GL6VG5ZVSZ7O7MITJP","download_json":"https://pith.science/pith/WTBRTO32GL6VG5ZVSZ7O7MITJP.json","view_paper":"https://pith.science/paper/WTBRTO32","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2003.02454&json=true","fetch_graph":"https://pith.science/api/pith-number/WTBRTO32GL6VG5ZVSZ7O7MITJP/graph.json","fetch_events":"https://pith.science/api/pith-number/WTBRTO32GL6VG5ZVSZ7O7MITJP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WTBRTO32GL6VG5ZVSZ7O7MITJP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WTBRTO32GL6VG5ZVSZ7O7MITJP/action/storage_attestation","attest_author":"https://pith.science/pith/WTBRTO32GL6VG5ZVSZ7O7MITJP/action/author_attestation","sign_citation":"https://pith.science/pith/WTBRTO32GL6VG5ZVSZ7O7MITJP/action/citation_signature","submit_replication":"https://pith.science/pith/WTBRTO32GL6VG5ZVSZ7O7MITJP/action/replication_record"}},"created_at":"2026-07-05T00:48:03.717956+00:00","updated_at":"2026-07-05T00:48:03.717956+00:00"}