Paper Citation Record · LEDGER

PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs

As of 18 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2606.22914.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2606.22914 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T09:21:40.944884Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

16 of 16 outbound references displayed

verified exact4
verified fuzzy0
unresolved8
parse uncertain0
malformed identifier2
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 48552ea1-4861-425b-85a3-79376ff4b5f4 · outbound

This paper cites GraphTTA: Test Time Adaptation on Graph Neural Networks.

PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs GraphTTA: Test Time Adaptation on Graph Neural Networks

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-07-04T09:49:45.020064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3fc6d8ac-5856-4d70-8ce9-e1b1ee3d6fc7 · outbound

This paper cites Do we really need compli- cated model architectures for temporal networks? In11th International Conference on Learning Representations, ICLR 2023,.

PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs Do we really need compli- cated model architectures for temporal networks? In11th International Conference on Learning Representations, ICLR 2023,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-26T09:21:40.944884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c5af522-af24-452b-83ed-f7c90d1f903a · outbound

This paper cites L., Leskovec, J., and Jurafsky, D.

PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs L., Leskovec, J., and Jurafsky, D

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-26T09:21:40.944884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2b821dd-fedf-4b9b-bc86-1992b0fe452e · outbound

This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.ACM Computing Surveys, 55(9):1–35, 2023a.

PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.ACM Computing Surveys, 55(9):1–35, 2023a

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-26T09:21:40.944884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 222c5234-00b1-4a06-b04a-877deea6b349 · outbound

This paper cites H., Lee, J.

PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs H., Lee, J

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-26T09:21:40.944884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19d88390-e13c-4a1a-86d9-905f553c1250 · outbound

This paper cites Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts.

PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T09:49:45.027438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dae729c2-6db4-4fd0-8aea-ffbfdcc4681a · outbound

This paper cites Graph Prompt Learning: A Comprehensive Survey and Beyond.

PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs Graph Prompt Learning: A Comprehensive Survey and Beyond

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T09:49:45.015075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8ccacc81-cd21-4cd7-b7f9-dbb2f233e1de · outbound

This paper cites Test-Time Training for Graph Neural Networks.

PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs Test-Time Training for Graph Neural Networks

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-04T09:49:45.010535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ccf95e38-3197-4219-8780-c30026a06146 · outbound

This paper cites and Fang, Y.

PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs and Fang, Y

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-26T09:21:40.944884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef7b81d4-f05d-43f9-96bb-c7880ed652fe · outbound

This paper cites Boundary- aware dual-stream network for vhr remote sensing images semantic segmentation.

PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs Boundary- aware dual-stream network for vhr remote sensing images semantic segmentation

Reference 10

Resolution: malformed identifier; arxiv_id, observed 2026-07-04T09:49:45.012799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 88056292-51cd-4e5f-94ff-373aeebe08c0 · outbound

This paper cites Discrete-time temporal network embedding via implicit hierarchical learning in hyperbolic space.

PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs Discrete-time temporal network embedding via implicit hierarchical learning in hyperbolic space

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-26T09:21:40.944884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee258b9d-bec6-4f86-827a-793745630894 · outbound

This paper cites Node-Time Conditional Prompt Learning In Dynamic Graphs.

PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs Node-Time Conditional Prompt Learning In Dynamic Graphs

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-04T09:49:45.017463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 91f17791-d3ed-4237-a7dc-a5d504cdb06b · outbound

This paper cites an unresolved cited work.

PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs Unresolved cited work

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-26T09:21:40.944884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 647960ad-014f-4efd-9abb-aabba981517c · outbound

This paper cites Moreover, Matcha can be combined with existing TTA methods to jointly handle structure and attribute shifts, achieving robust performance under diverse distribution shift settings.

PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs Moreover, Matcha can be combined with existing TTA methods to jointly handle structure and attribute shifts, achieving robust performance under diverse distribution shift settings

Reference 14

Resolution: malformed identifier; arxiv_id, observed 2026-07-04T09:49:45.022524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9c370bc7-0d6f-481b-9332-9f30a7a2f2c9 · outbound

This paper cites This snapshot-based formulation aligns better with the data collection mechanisms of many real-world systems, making DTDGs a practical choice for modeling long-term data evolution.

PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs This snapshot-based formulation aligns better with the data collection mechanisms of many real-world systems, making DTDGs a practical choice for modeling long-term data evolution

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-26T09:21:40.944884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0193ff59-90ce-47c4-8e58-af07add1e90f · outbound

This paper cites an unresolved cited work.

PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs Unresolved cited work

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-04T09:49:45.024815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.