Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T19:12:29.020671Z
Paper Citation Record · LEDGER
IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs
As of 21 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2411.10957.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
13 of 13 outbound references displayed
- verified exact2
- verified fuzzy7
- unresolved4
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation fb4a98ac-e048-4d14-abf2-1fef663c7db9 · outbound
This paper cites Data for t = 2018 and t = 2019 were excluded, and no scaling corrections were applied.
IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs Data for t = 2018 and t = 2019 were excluded, and no scaling corrections were applied
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T19:12:29.172115Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation f31e0727-0eb0-49fb-b6e5-4170abeaf1b5 · outbound
This paper cites A.2 T OY EXPERIMENT The purpose of toy experiment was to compare test accuracy obtained when dataset was split chrono- logically and split randomly regardless of time information.
IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs A.2 T OY EXPERIMENT The purpose of toy experiment was to compare test accuracy obtained when dataset was split chrono- logically and split randomly regardless of time information
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T19:12:29.158066Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation cb904834-6b1e-4110-9038-427afe6699c1 · outbound
This paper cites SIGN: Scalable Inception Graph Neural Networks.
IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs SIGN: Scalable Inception Graph Neural Networks
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:12:28.984329Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 338cf7cf-d674-4ec3-8daf-180a019e5a45 · outbound
This paper cites Heterogeneous Graph Neural Networks with Loss-decrease-aware Curriculum Learning.
IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs Heterogeneous Graph Neural Networks with Loss-decrease-aware Curriculum Learning
Reference 6
- Resolution
- verified exact
- local_arxiv, observed 2026-08-12T19:12:29.068772Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 1cab827f-99ed-4d6f-92d2-005c580582f0 · outbound
This paper cites The figure on the right considers only the 15 labels with the most nodes, redrawing the graph for clarity.
IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs The figure on the right considers only the 15 labels with the most nodes, redrawing the graph for clarity
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T19:12:29.185377Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation a1e727f6-4495-403d-a3e8-55aa55470db8 · outbound
This paper cites The performance metric is accuracy, representing the proportion of correctly labeled nodes among all test nodes.
IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs The performance metric is accuracy, representing the proportion of correctly labeled nodes among all test nodes
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T19:12:29.144862Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 4f9f1fa1-152e-480c-bdd2-680867842431 · outbound
This paper cites an unresolved cited work.
IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs Unresolved cited work
Reference 13
- Resolution
- unresolved
- raw_fallback, observed 2026-08-12T19:12:29.116861Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation df45d156-6b4a-48d2-adcf-f4137f3ae661 · outbound
This paper cites Efficient Heterogeneous Graph Learning via Random Projection.
IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs Efficient Heterogeneous Graph Learning via Random Projection
Reference 1983
- Resolution
- verified exact
- local_arxiv, observed 2026-08-12T19:12:29.102808Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 02d1c187-d27d-4e88-9f76-4341a23f1971 · outbound
This paper cites Node feature extraction by self-supervised multi-scale neighborhood predic- tion.
IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs Node feature extraction by self-supervised multi-scale neighborhood predic- tion
Reference 2010
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T19:12:29.210893Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation c7b96676-a6ad-4643-9d72-cb1ff2d115b1 · outbound
This paper cites Temporal graph neural networks for social recommendation.
IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs Temporal graph neural networks for social recommendation
Reference 2017
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:12:28.967726Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3b549dac-4dbe-4e11-8c0a-37f9f829b9bb · outbound
This paper cites Theoretical analysis of domain adaptation with optimal transport.
IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs Theoretical analysis of domain adaptation with optimal transport
Reference 2020
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T19:12:29.198360Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation a739f1e5-f047-41f4-9641-b68d56dbe3fb · outbound
This paper cites approximate of expectation.
IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs approximate of expectation
Reference 2023
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T19:12:29.131034Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 76805763-43db-45dc-860f-a275c1e929bb · outbound
This paper cites Loss-aware Curriculum Learning for Heterogeneous Graph Neural Networks.
IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs Loss-aware Curriculum Learning for Heterogeneous Graph Neural Networks
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:12:28.994090Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.