{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:XUQFBIUYIF2HZLRTL7G7BESAIP","short_pith_number":"pith:XUQFBIUY","schema_version":"1.0","canonical_sha256":"bd2050a29841747cae335fcdf0924043f7fe353a81862f698c6b8fcbffb7f6bc","source":{"kind":"arxiv","id":"1711.01191","version":2},"attestation_state":"computed","paper":{"title":"Learning flexible representations of stochastic processes on graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"eess.SP","authors_text":"Addison Bohannon, Brian Sadler, Radu Balan","submitted_at":"2017-11-03T14:45:50Z","abstract_excerpt":"Graph convolutional networks adapt the architecture of convolutional neural networks to learn rich representations of data supported on arbitrary graphs by replacing the convolution operations of convolutional neural networks with graph-dependent linear operations. However, these graph-dependent linear operations are developed for scalar functions supported on undirected graphs. We propose a class of linear operations for stochastic (time-varying) processes on directed (or undirected) graphs to be used in graph convolutional networks. We propose a parameterization of such linear operations usi"},"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":"1711.01191","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2017-11-03T14:45:50Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"23a5efb40d408787ffe2f4f2b1de143e113f81bc7b4fbf8d2c5663eef19a26c9","abstract_canon_sha256":"9e796b1e30b5c16d3268dea368c07569d2faa232cdd361d2f50ab83b805ab4b3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:21:21.791105Z","signature_b64":"pInG1zdiS1OUsg0d+0IzDu478RkQqivW6ga6I0x0f0cm/H/m+/m9Ew1p8fyPNzAKsQJlyhxQE5tv55zX86wcDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bd2050a29841747cae335fcdf0924043f7fe353a81862f698c6b8fcbffb7f6bc","last_reissued_at":"2026-05-18T00:21:21.790459Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:21:21.790459Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning flexible representations of stochastic processes on graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"eess.SP","authors_text":"Addison Bohannon, Brian Sadler, Radu Balan","submitted_at":"2017-11-03T14:45:50Z","abstract_excerpt":"Graph convolutional networks adapt the architecture of convolutional neural networks to learn rich representations of data supported on arbitrary graphs by replacing the convolution operations of convolutional neural networks with graph-dependent linear operations. However, these graph-dependent linear operations are developed for scalar functions supported on undirected graphs. We propose a class of linear operations for stochastic (time-varying) processes on directed (or undirected) graphs to be used in graph convolutional networks. We propose a parameterization of such linear operations usi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.01191","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1711.01191","created_at":"2026-05-18T00:21:21.790559+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.01191v2","created_at":"2026-05-18T00:21:21.790559+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.01191","created_at":"2026-05-18T00:21:21.790559+00:00"},{"alias_kind":"pith_short_12","alias_value":"XUQFBIUYIF2H","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_16","alias_value":"XUQFBIUYIF2HZLRT","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_8","alias_value":"XUQFBIUY","created_at":"2026-05-18T12:31:56.362134+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XUQFBIUYIF2HZLRTL7G7BESAIP","json":"https://pith.science/pith/XUQFBIUYIF2HZLRTL7G7BESAIP.json","graph_json":"https://pith.science/api/pith-number/XUQFBIUYIF2HZLRTL7G7BESAIP/graph.json","events_json":"https://pith.science/api/pith-number/XUQFBIUYIF2HZLRTL7G7BESAIP/events.json","paper":"https://pith.science/paper/XUQFBIUY"},"agent_actions":{"view_html":"https://pith.science/pith/XUQFBIUYIF2HZLRTL7G7BESAIP","download_json":"https://pith.science/pith/XUQFBIUYIF2HZLRTL7G7BESAIP.json","view_paper":"https://pith.science/paper/XUQFBIUY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.01191&json=true","fetch_graph":"https://pith.science/api/pith-number/XUQFBIUYIF2HZLRTL7G7BESAIP/graph.json","fetch_events":"https://pith.science/api/pith-number/XUQFBIUYIF2HZLRTL7G7BESAIP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XUQFBIUYIF2HZLRTL7G7BESAIP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XUQFBIUYIF2HZLRTL7G7BESAIP/action/storage_attestation","attest_author":"https://pith.science/pith/XUQFBIUYIF2HZLRTL7G7BESAIP/action/author_attestation","sign_citation":"https://pith.science/pith/XUQFBIUYIF2HZLRTL7G7BESAIP/action/citation_signature","submit_replication":"https://pith.science/pith/XUQFBIUYIF2HZLRTL7G7BESAIP/action/replication_record"}},"created_at":"2026-05-18T00:21:21.790559+00:00","updated_at":"2026-05-18T00:21:21.790559+00:00"}