Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2408.04713.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T00:49:58.781862Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T07:26:45.720095Z
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 7d5db003-da37-4076-8b29-9ca03ef24ee4 · inbound
Advancing Intelligent Sequence Modeling: Evolution, Trade-offs, and Applications of State- Space Architectures from S4 to Mamba cites this paper.
Advancing Intelligent Sequence Modeling: Evolution, Trade-offs, and Applications of State- Space Architectures from S4 to Mamba DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models
Reference 30
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T22:12:11.809800Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation bd25f159-88d6-49df-a129-6264fb9225a1 · inbound
Are We Really Measuring Progress? Transferring Insights from Evaluating Recommender Systems to Temporal Link Prediction cites this paper.
Are We Really Measuring Progress? Transferring Insights from Evaluating Recommender Systems to Temporal Link Prediction DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:49:58.781862Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 91217633-e8f0-4768-af84-77e8cdf04c9c · inbound
DuConTE: Dual-Granularity Text Encoder with Topology-Constrained Attention for Text-attributed Graphs cites this paper.
DuConTE: Dual-Granularity Text Encoder with Topology-Constrained Attention for Text-attributed Graphs DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models
Reference 85
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-10T05:51:09.928520Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 633e5378-5c65-4797-a1e8-cb698ba0f5ba · inbound
Learning Long Range Spatio-Temporal Representations over Continuous Time Dynamic Graphs with State Space Models cites this paper.
Learning Long Range Spatio-Temporal Representations over Continuous Time Dynamic Graphs with State Space Models DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T07:26:45.721702Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.