{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:PJFU4H7XQEPXNKRIZ4J4WBMPNV","short_pith_number":"pith:PJFU4H7X","schema_version":"1.0","canonical_sha256":"7a4b4e1ff7811f76aa28cf13cb058f6d415b5a923c221746befc6dc5017323d9","source":{"kind":"arxiv","id":"1902.06815","version":1},"attestation_state":"computed","paper":{"title":"The State of the Art in Multilayer Network Visualization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SI","authors_text":"Benoit Otjacques (LIST), Bruno Pinaud (LaBRI, Fintan Mcgee (LIST), Guy Melan\\c{c}on (LaBRI, Mohammad Ghoniem (LIST), UB)","submitted_at":"2019-02-12T16:11:08Z","abstract_excerpt":"Modelling relationships between entities in real-world systems with a simple graph is a standard approach. However, reality is better embraced as several interdependent subsystems (or layers). Recently the concept of a multilayer network model has emerged from the field of complex systems. This model can be applied to a wide range of real-world datasets. Examples of multilayer networks can be found in the domains of life sciences, sociology, digital humanities and more. Within the domain of graph visualization there are many systems which visualize datasets having many characteristics of multi"},"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":"1902.06815","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2019-02-12T16:11:08Z","cross_cats_sorted":[],"title_canon_sha256":"23d23f52d97fc4c96b2db086ca0531aab4a03d25e85bf83fc5180e269f7dea2b","abstract_canon_sha256":"31c0b9fb4b30c34933cff95ec047abf35aa74168e4929d0355a02a51556a25f9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:53:40.727466Z","signature_b64":"ras8xwQXOP3YlwxzIyc94EzNrhYIrkVc4B6VIHhBUgQEqtHE3TZ0MxSC8gnAdRHkUafOLzhbm3Ku2nbHovoGBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a4b4e1ff7811f76aa28cf13cb058f6d415b5a923c221746befc6dc5017323d9","last_reissued_at":"2026-05-17T23:53:40.726804Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:53:40.726804Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The State of the Art in Multilayer Network Visualization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SI","authors_text":"Benoit Otjacques (LIST), Bruno Pinaud (LaBRI, Fintan Mcgee (LIST), Guy Melan\\c{c}on (LaBRI, Mohammad Ghoniem (LIST), UB)","submitted_at":"2019-02-12T16:11:08Z","abstract_excerpt":"Modelling relationships between entities in real-world systems with a simple graph is a standard approach. However, reality is better embraced as several interdependent subsystems (or layers). Recently the concept of a multilayer network model has emerged from the field of complex systems. This model can be applied to a wide range of real-world datasets. Examples of multilayer networks can be found in the domains of life sciences, sociology, digital humanities and more. Within the domain of graph visualization there are many systems which visualize datasets having many characteristics of multi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.06815","kind":"arxiv","version":1},"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":"1902.06815","created_at":"2026-05-17T23:53:40.726912+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.06815v1","created_at":"2026-05-17T23:53:40.726912+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.06815","created_at":"2026-05-17T23:53:40.726912+00:00"},{"alias_kind":"pith_short_12","alias_value":"PJFU4H7XQEPX","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"PJFU4H7XQEPXNKRI","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"PJFU4H7X","created_at":"2026-05-18T12:33:24.271573+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/PJFU4H7XQEPXNKRIZ4J4WBMPNV","json":"https://pith.science/pith/PJFU4H7XQEPXNKRIZ4J4WBMPNV.json","graph_json":"https://pith.science/api/pith-number/PJFU4H7XQEPXNKRIZ4J4WBMPNV/graph.json","events_json":"https://pith.science/api/pith-number/PJFU4H7XQEPXNKRIZ4J4WBMPNV/events.json","paper":"https://pith.science/paper/PJFU4H7X"},"agent_actions":{"view_html":"https://pith.science/pith/PJFU4H7XQEPXNKRIZ4J4WBMPNV","download_json":"https://pith.science/pith/PJFU4H7XQEPXNKRIZ4J4WBMPNV.json","view_paper":"https://pith.science/paper/PJFU4H7X","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.06815&json=true","fetch_graph":"https://pith.science/api/pith-number/PJFU4H7XQEPXNKRIZ4J4WBMPNV/graph.json","fetch_events":"https://pith.science/api/pith-number/PJFU4H7XQEPXNKRIZ4J4WBMPNV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PJFU4H7XQEPXNKRIZ4J4WBMPNV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PJFU4H7XQEPXNKRIZ4J4WBMPNV/action/storage_attestation","attest_author":"https://pith.science/pith/PJFU4H7XQEPXNKRIZ4J4WBMPNV/action/author_attestation","sign_citation":"https://pith.science/pith/PJFU4H7XQEPXNKRIZ4J4WBMPNV/action/citation_signature","submit_replication":"https://pith.science/pith/PJFU4H7XQEPXNKRIZ4J4WBMPNV/action/replication_record"}},"created_at":"2026-05-17T23:53:40.726912+00:00","updated_at":"2026-05-17T23:53:40.726912+00:00"}