Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1911.07893.
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 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:53:36.988846Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-17T17:04:50.345981Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation b5da204b-60a5-4265-a66d-21ea34f5075c · inbound
Temporal Graph Networks for Deep Learning on Dynamic Graphs cites this paper.
Temporal Graph Networks for Deep Learning on Dynamic Graphs Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition
Reference 152
- Resolution
- verified exact
- arxiv_id, observed 2026-05-17T17:04:50.349338Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 7a423df1-3302-472b-9373-9abe9239c426 · inbound
VITA: Versatile Time Representation Learning for Temporal Hyper-Relational Knowledge Graphs cites this paper.
VITA: Versatile Time Representation Learning for Temporal Hyper-Relational Knowledge Graphs Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition
Reference 58
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:53:36.988846Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 63f5bdcf-44db-459a-b3d4-8b0be9b8a789 · inbound
Towards Improving Long-Tail Entity Predictions in Temporal Knowledge Graphs through Global Similarity and Weighted Sampling cites this paper.
Towards Improving Long-Tail Entity Predictions in Temporal Knowledge Graphs through Global Similarity and Weighted Sampling Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition
Reference 37
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:10:21.712332Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7bbb1918-de53-4481-802d-a206e193af4f · inbound
Load Forecasting on A Highly Sparse Electrical Load Dataset Using Gaussian Interpolation cites this paper.
Load Forecasting on A Highly Sparse Electrical Load Dataset Using Gaussian Interpolation Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition
Reference 48
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T17:39:05.556234Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 86d087fe-6e52-4eab-bf6d-fe6881b9dde1 · inbound
Self-Aware Vector Embeddings for Retrieval-Augmented Generation: A Neuroscience-Inspired Framework for Temporal, Confidence-Weighted, and Relational Knowledge cites this paper.
Self-Aware Vector Embeddings for Retrieval-Augmented Generation: A Neuroscience-Inspired Framework for Temporal, Confidence-Weighted, and Relational Knowledge Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition
Reference 6
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T14:11:05.775569Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.