{"as_of":"2026-08-12T21:22:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a4dafb5bbe5ae9fc2d0081da64481e28d09d8e978ae01948a65eb61a11e73f4d","coverage":[{"denominator":18,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":18,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T19:14:11.211176Z","state":"measured"},{"denominator":18,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":18,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2412.07809/citation-record","integrity":"/paper/2412.07809/integrity","json":"/paper/2412.07809/citation-record.json","paper":"/paper/2412.07809"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:11.127731Z","title":", \" * write output.state after.block = add.period write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.07809","last_updated":"2024-12-10T01:12:51Z","snapshot_observed_at":"2026-08-11T19:06:34.630603Z","submitted_at":"2024-12-10T01:12:51Z","title":"Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-11T19:14:11.127731Z"},"links":{"citing_paper":"/paper/2412.07809"},"observation_digest":"sha256:7fd6b240788213430affeb1bda2b995f3cf940c085ed225742845bc874a960e0","observation_id":"d44aff5e-f473-4f21-a600-fd8884de17b1","resolution":{"observed_at":"2026-08-11T19:14:11.127731Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:11.133710Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.07809","last_updated":"2024-12-10T01:12:51Z","snapshot_observed_at":"2026-08-11T19:06:34.630603Z","submitted_at":"2024-12-10T01:12:51Z","title":"Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-11T19:14:11.133710Z"},"links":{"citing_paper":"/paper/2412.07809"},"observation_digest":"sha256:8dfea73831532ca6434c093d4df374ed3ada08718e2a5407e9b36e1b9f2fe801","observation_id":"14c8f4d6-eb94-4def-b65b-f9aa9c48b65f","resolution":{"observed_at":"2026-08-11T19:14:11.133710Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:11.553925Z","title":null,"venue":null,"work_id":"da1f61b3-e8de-483e-a540-29e61e7bfae3","year":2020},"citing_paper":{"arxiv_id":"2412.07809","last_updated":"2024-12-10T01:12:51Z","snapshot_observed_at":"2026-08-11T19:06:34.630603Z","submitted_at":"2024-12-10T01:12:51Z","title":"Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-11T19:14:11.139500Z"},"links":{"citing_paper":"/paper/2412.07809"},"observation_digest":"sha256:5c85a1b4dd7481f2b06639cd0bb0dbe6def9192e718fa5bbc2e18afd99ca4ce5","observation_id":"15d69993-0d89-4a73-a266-5bac49ed10e9","resolution":{"observed_at":"2026-08-11T19:14:11.559424Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:11.538746Z","title":"R.; Wu, C","venue":null,"work_id":"35cc60c9-2091-4c10-9575-9668a7c2b01c","year":2023},"citing_paper":{"arxiv_id":"2412.07809","last_updated":"2024-12-10T01:12:51Z","snapshot_observed_at":"2026-08-11T19:06:34.630603Z","submitted_at":"2024-12-10T01:12:51Z","title":"Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-11T19:14:11.144331Z"},"links":{"citing_paper":"/paper/2412.07809"},"observation_digest":"sha256:076e4866b73ce040ccd57439af7e57516b0f30140760a21868b76481ec012364","observation_id":"202a59fb-7639-4ebd-9326-ee489f5a4f72","resolution":{"observed_at":"2026-08-11T19:14:11.543351Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:11.523530Z","title":null,"venue":null,"work_id":"5f297671-ee43-49d4-a7e1-83a4e16b4f04","year":2021},"citing_paper":{"arxiv_id":"2412.07809","last_updated":"2024-12-10T01:12:51Z","snapshot_observed_at":"2026-08-11T19:06:34.630603Z","submitted_at":"2024-12-10T01:12:51Z","title":"Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-11T19:14:11.150755Z"},"links":{"citing_paper":"/paper/2412.07809"},"observation_digest":"sha256:f1b0866c2040432bbf13bfdb107714d837a1c712abcd8a64decb7cca325d7272","observation_id":"27309995-f551-4ef3-9509-13b2885c9a76","resolution":{"observed_at":"2026-08-11T19:14:11.527970Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:11.508147Z","title":null,"venue":null,"work_id":"ca8c7d02-e25c-4c1a-97f4-08ee62645819","year":2019},"citing_paper":{"arxiv_id":"2412.07809","last_updated":"2024-12-10T01:12:51Z","snapshot_observed_at":"2026-08-11T19:06:34.630603Z","submitted_at":"2024-12-10T01:12:51Z","title":"Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-11T19:14:11.155421Z"},"links":{"citing_paper":"/paper/2412.07809"},"observation_digest":"sha256:4f761d7c9c24cbf9540d9d775637ac0be9ee16e61f73e95a292696aa755dfd99","observation_id":"14926160-2ea4-4482-aca9-0e5d6ede355b","resolution":{"observed_at":"2026-08-11T19:14:11.513623Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:11.493642Z","title":null,"venue":null,"work_id":"3bb818f0-3458-47cc-93b2-ec9385a6dd74","year":2022},"citing_paper":{"arxiv_id":"2412.07809","last_updated":"2024-12-10T01:12:51Z","snapshot_observed_at":"2026-08-11T19:06:34.630603Z","submitted_at":"2024-12-10T01:12:51Z","title":"Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-11T19:14:11.159961Z"},"links":{"citing_paper":"/paper/2412.07809"},"observation_digest":"sha256:89294fab26113ebaa56b29a8862a595b0d60c2b5bcf017523bc132a669773224","observation_id":"9a80b99e-3f05-4bb8-a421-121cdbd51a29","resolution":{"observed_at":"2026-08-11T19:14:11.498326Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:11.164360Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.07809","last_updated":"2024-12-10T01:12:51Z","snapshot_observed_at":"2026-08-11T19:06:34.630603Z","submitted_at":"2024-12-10T01:12:51Z","title":"Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-11T19:14:11.164360Z"},"links":{"citing_paper":"/paper/2412.07809"},"observation_digest":"sha256:8a6a85f5d07dfa8a9e66dc669a88e8820f6365dd618a55362dea9294319e3ce0","observation_id":"a9cb9a1f-5e00-47af-91bd-602f79110bb8","resolution":{"observed_at":"2026-08-11T19:14:11.164360Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:11.479101Z","title":null,"venue":null,"work_id":"1b6f231f-7446-40d0-a1b6-b4c88e639f6f","year":2020},"citing_paper":{"arxiv_id":"2412.07809","last_updated":"2024-12-10T01:12:51Z","snapshot_observed_at":"2026-08-11T19:06:34.630603Z","submitted_at":"2024-12-10T01:12:51Z","title":"Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-11T19:14:11.169365Z"},"links":{"citing_paper":"/paper/2412.07809"},"observation_digest":"sha256:30e381fb792d4a290acf0f6785a159edf0a7eb71ec3a1c4a8d9e01ccda8d9d53","observation_id":"7b18bee6-04a1-4594-b9b3-e08e723d2f27","resolution":{"observed_at":"2026-08-11T19:14:11.483699Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:11.463486Z","title":null,"venue":null,"work_id":"2ed63f95-04f9-482c-9cf8-96e02305a199","year":2022},"citing_paper":{"arxiv_id":"2412.07809","last_updated":"2024-12-10T01:12:51Z","snapshot_observed_at":"2026-08-11T19:06:34.630603Z","submitted_at":"2024-12-10T01:12:51Z","title":"Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-11T19:14:11.173853Z"},"links":{"citing_paper":"/paper/2412.07809"},"observation_digest":"sha256:9462295afb9f58f386169fd64fe98b6193991c8c383fa7df04550c24d39844e2","observation_id":"a930f53f-6e87-4d9d-942c-59ba086beb9a","resolution":{"observed_at":"2026-08-11T19:14:11.468939Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:11.449177Z","title":null,"venue":null,"work_id":"a3c1b7ab-c4da-4380-bea0-b6dc58edc0da","year":2020},"citing_paper":{"arxiv_id":"2412.07809","last_updated":"2024-12-10T01:12:51Z","snapshot_observed_at":"2026-08-11T19:06:34.630603Z","submitted_at":"2024-12-10T01:12:51Z","title":"Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-11T19:14:11.178251Z"},"links":{"citing_paper":"/paper/2412.07809"},"observation_digest":"sha256:eb767ed6ad45e811bfc8aa98a3d8a8bcfeb0ed69329869046fc93a0a2bf7d1ff","observation_id":"4d05cd60-c349-4d9d-b1c2-79706506f78b","resolution":{"observed_at":"2026-08-11T19:14:11.453804Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.03748","last_updated":"2019-01-22T18:47:12Z","snapshot_observed_at":"2026-07-06T06:49:24.960992Z","submitted_at":"2018-07-10T16:52:11Z","title":"Representation Learning with Contrastive Predictive Coding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.03748","snapshot_observed_at":"2026-08-11T19:14:11.182558Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.07809","last_updated":"2024-12-10T01:12:51Z","snapshot_observed_at":"2026-08-11T19:06:34.630603Z","submitted_at":"2024-12-10T01:12:51Z","title":"Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-11T19:14:11.182558Z"},"links":{"cited_paper":"/paper/1807.03748","citing_paper":"/paper/2412.07809"},"observation_digest":"sha256:1c6f84e43c8b8deb01f8207b69154c98c88c3b881841f7beedc67611e5313cdf","observation_id":"70d3a33b-fe35-4f4a-b5d9-3493b6851144","resolution":{"observed_at":"2026-08-11T19:14:11.182558Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:11.432324Z","title":null,"venue":null,"work_id":"b95b1d94-7549-4605-9e39-946f751ef77d","year":2021},"citing_paper":{"arxiv_id":"2412.07809","last_updated":"2024-12-10T01:12:51Z","snapshot_observed_at":"2026-08-11T19:06:34.630603Z","submitted_at":"2024-12-10T01:12:51Z","title":"Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-11T19:14:11.187956Z"},"links":{"citing_paper":"/paper/2412.07809"},"observation_digest":"sha256:c723d6753303df42a780ada9da93bdbd4e4f1d110925633d84217d466c818f96","observation_id":"1a08b818-f85b-457e-8bca-c62434053675","resolution":{"observed_at":"2026-08-11T19:14:11.438301Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:11.416984Z","title":null,"venue":null,"work_id":"b1059e5e-dcbc-47a8-b256-2ed7118fdbed","year":2022},"citing_paper":{"arxiv_id":"2412.07809","last_updated":"2024-12-10T01:12:51Z","snapshot_observed_at":"2026-08-11T19:06:34.630603Z","submitted_at":"2024-12-10T01:12:51Z","title":"Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-11T19:14:11.193024Z"},"links":{"citing_paper":"/paper/2412.07809"},"observation_digest":"sha256:1d205fde65f4d726ce22218295154b83b670a7e1f3554cedce589259198c4a2a","observation_id":"f691ad90-d836-4a2e-b045-f80a001160b4","resolution":{"observed_at":"2026-08-11T19:14:11.421275Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:11.402192Z","title":null,"venue":null,"work_id":"e8f84c6b-0709-4366-94c2-36920b7852ac","year":2020},"citing_paper":{"arxiv_id":"2412.07809","last_updated":"2024-12-10T01:12:51Z","snapshot_observed_at":"2026-08-11T19:06:34.630603Z","submitted_at":"2024-12-10T01:12:51Z","title":"Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-11T19:14:11.197337Z"},"links":{"citing_paper":"/paper/2412.07809"},"observation_digest":"sha256:38b1a1fe011cb45d4bb1425d4275ab1d8bd45f95f974ef93ceac9f72a9ae9b9d","observation_id":"1554206d-2dc2-45ef-9f7e-2deea1fab016","resolution":{"observed_at":"2026-08-11T19:14:11.406721Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:11.386034Z","title":null,"venue":null,"work_id":"f1725c73-040c-4dc3-857e-50ae5b0b9f4a","year":2023},"citing_paper":{"arxiv_id":"2412.07809","last_updated":"2024-12-10T01:12:51Z","snapshot_observed_at":"2026-08-11T19:06:34.630603Z","submitted_at":"2024-12-10T01:12:51Z","title":"Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-11T19:14:11.202107Z"},"links":{"citing_paper":"/paper/2412.07809"},"observation_digest":"sha256:9749fbe3cff9e9280f370ae1f5d114a99edaeee1a66faa668a25621959dd951f","observation_id":"b1dacd64-b3ce-4e69-a749-817804fe40e1","resolution":{"observed_at":"2026-08-11T19:14:11.391543Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:11.370975Z","title":null,"venue":null,"work_id":"77c0abc5-b923-434a-bf2b-00408b937245","year":2021},"citing_paper":{"arxiv_id":"2412.07809","last_updated":"2024-12-10T01:12:51Z","snapshot_observed_at":"2026-08-11T19:06:34.630603Z","submitted_at":"2024-12-10T01:12:51Z","title":"Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-11T19:14:11.206600Z"},"links":{"citing_paper":"/paper/2412.07809"},"observation_digest":"sha256:61945c7bf7bada7ce9230fcd6cd42d0389a4177a69b32aef86c7e23d0c13ea5d","observation_id":"19e2d9f1-70b0-4bc9-8e49-34218ef237bb","resolution":{"observed_at":"2026-08-11T19:14:11.375652Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:11.353014Z","title":null,"venue":null,"work_id":"95b92a9a-4071-4699-b090-021b223fcb05","year":2020},"citing_paper":{"arxiv_id":"2412.07809","last_updated":"2024-12-10T01:12:51Z","snapshot_observed_at":"2026-08-11T19:06:34.630603Z","submitted_at":"2024-12-10T01:12:51Z","title":"Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-11T19:14:11.211176Z"},"links":{"citing_paper":"/paper/2412.07809"},"observation_digest":"sha256:2310b64884bf30ae0d6a6918af69293b77bc5c3de87f0cc86c56f9cfb15c1d09","observation_id":"26c8e24f-1b30-48e3-a06a-d2e4aaeb4358","resolution":{"observed_at":"2026-08-11T19:14:11.360215Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.07809","last_updated":"2024-12-10T01:12:51Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-11T19:06:34.630603Z","submitted_at":"2024-12-10T01:12:51Z","title":"Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning"},"reference_resolution":{"displayed":18,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":0,"verified_fuzzy":1},"total_outbound_references":18},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2412.07809."}