Paper Citation Record · LEDGER

GCAL: Adapting Graph Models to Evolving Domain Shifts

As of 7 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 1 inbound Pith citation observation for arXiv:2505.16860.

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

pith.paper-citation-record.v1
2505.16860 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:00:25.993970Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T08:31:25.511131Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

28 of 28 outbound references displayed

verified exact1
verified fuzzy11
unresolved16
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 91786ab7-24c9-47d6-9f8c-f215a95c9fab · outbound

This paper cites Multimodal continual graph learning with neural architecture search.

GCAL: Adapting Graph Models to Evolving Domain Shifts Multimodal continual graph learning with neural architecture search

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:00:30.240738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f99dc326-35d7-4a88-92c5-b4fa4a07efc3 · outbound

This paper cites Training generative neural networks via Maximum Mean Discrepancy optimization.

GCAL: Adapting Graph Models to Evolving Domain Shifts Training generative neural networks via Maximum Mean Discrepancy optimization

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T15:00:23.609463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e8569ab-dcd0-45e5-a572-8a2d27e1dda5 · outbound

This paper cites reflects the dynamic and challenging nature of financial transactions.

GCAL: Adapting Graph Models to Evolving Domain Shifts reflects the dynamic and challenging nature of financial transactions

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:00:28.747797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 04b392a0-7b25-4adb-a996-b4726a4a3a8b · outbound

This paper cites Using our framework demonstrates remarkable enhancements, showing the effectiveness of our proposed techniques.

GCAL: Adapting Graph Models to Evolving Domain Shifts Using our framework demonstrates remarkable enhancements, showing the effectiveness of our proposed techniques

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:00:28.452742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 34bf9bef-c29b-4e59-bd15-853cede3ed31 · outbound

This paper cites Revisiting Batch Normalization For Practical Domain Adaptation.

GCAL: Adapting Graph Models to Evolving Domain Shifts Revisiting Batch Normalization For Practical Domain Adaptation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T15:00:23.988205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82de5f34-8764-4649-a06a-9c5dec145aab · outbound

This paper cites PUMA: Efficient Continual Graph Learning for Node Classification with Graph Condensation.

GCAL: Adapting Graph Models to Evolving Domain Shifts PUMA: Efficient Continual Graph Learning for Node Classification with Graph Condensation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T15:00:24.115372Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3d820a7-d668-49c1-b049-83fb3dca8f6d · outbound

This paper cites Active Learning for Convolutional Neural Networks: A Core-Set Approach.

GCAL: Adapting Graph Models to Evolving Domain Shifts Active Learning for Convolutional Neural Networks: A Core-Set Approach

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T15:00:24.216981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2848e6d4-fbf5-4fb9-896e-2b15b6319fd1 · outbound

This paper cites Single- view graph contrastive learning with soft neighborhood awareness.

GCAL: Adapting Graph Models to Evolving Domain Shifts Single- view graph contrastive learning with soft neighborhood awareness

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:00:29.399133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3f5f9ef4-3afe-4efa-be06-1f2fb0535dfb · outbound

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

GCAL: Adapting Graph Models to Evolving Domain Shifts Graph Prompt Learning: A Comprehensive Survey and Beyond

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T15:00:24.447922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a76675d-c9cd-4959-ad29-b41e42cc68cf · outbound

This paper cites Graph Attention Networks.

GCAL: Adapting Graph Models to Evolving Domain Shifts Graph Attention Networks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T15:00:24.504235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32c9db38-3323-4261-8c82-26466ef0831f · outbound

This paper cites Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis.

GCAL: Adapting Graph Models to Evolving Domain Shifts Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T15:00:24.597659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b94a474-e62e-4fcf-92b1-e2fbeb4d3952 · outbound

This paper cites log P ( bGt|Gt, Zt) Q( bGt) # + KL(P ( bGt) ∥ Q( bGt)) ≥ −E bGt,Gt,Zt.

GCAL: Adapting Graph Models to Evolving Domain Shifts log P ( bGt|Gt, Zt) Q( bGt) # + KL(P ( bGt) ∥ Q( bGt)) ≥ −E bGt,Gt,Zt

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:00:29.227242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d793ae3a-0683-4638-b6a1-4310f2107687 · outbound

This paper cites These networks vary greatly in size, density, and degree distribution.

GCAL: Adapting Graph Models to Evolving Domain Shifts These networks vary greatly in size, density, and degree distribution

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:00:29.102940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3fbf09dd-aed9-484c-9e18-46a8da6b3a70 · outbound

This paper cites For baselines not originally designed for graphs, their architectures have been adapted to GCNs to ensure consistency in evaluation.

GCAL: Adapting Graph Models to Evolving Domain Shifts For baselines not originally designed for graphs, their architectures have been adapted to GCNs to ensure consistency in evaluation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:00:28.958972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation db03ef39-579b-42d4-ac50-15fd1f6f6430 · outbound

This paper cites an unresolved cited work.

GCAL: Adapting Graph Models to Evolving Domain Shifts Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:00:28.251377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c533165d-e3f1-4fbb-89f0-750e98d06cce · outbound

This paper cites Continual Test-Time Adaptation (CTTA), a critical facet of Continual Domain Adaptation, addresses the unique demands of non-static domains.

GCAL: Adapting Graph Models to Evolving Domain Shifts Continual Test-Time Adaptation (CTTA), a critical facet of Continual Domain Adaptation, addresses the unique demands of non-static domains

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:00:27.380817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 059c9460-ce22-4cd9-a22c-e4b004dc54ec · outbound

This paper cites an unresolved cited work.

GCAL: Adapting Graph Models to Evolving Domain Shifts Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:00:27.072461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8a55b653-1581-4573-8c05-7428d78d7dc9 · outbound

This paper cites Among recent innovations, GCDM (Liu et al., 2022), introduce graph-specific distribution alignment to enhance condensation effectiveness.

GCAL: Adapting Graph Models to Evolving Domain Shifts Among recent innovations, GCDM (Liu et al., 2022), introduce graph-specific distribution alignment to enhance condensation effectiveness

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:00:26.808347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 623b4364-3634-4e44-a1b9-04951e9535c5 · outbound

This paper cites an unresolved cited work.

GCAL: Adapting Graph Models to Evolving Domain Shifts Unresolved cited work

Reference 2015

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:00:27.649998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1c3e4c0c-aabe-47a1-abe1-acb6506d5e06 · outbound

This paper cites an unresolved cited work.

GCAL: Adapting Graph Models to Evolving Domain Shifts Unresolved cited work

Reference 2016

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:00:29.691403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b1f2a8aa-e7a4-4214-a609-546641e276a3 · outbound

This paper cites A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation.

GCAL: Adapting Graph Models to Evolving Domain Shifts A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T15:00:23.747834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f70be6f-f124-431e-b1c8-1a1962e88b3a · outbound

This paper cites Topology-aware Embedding Memory for Continual Learning on Expanding Networks.

GCAL: Adapting Graph Models to Evolving Domain Shifts Topology-aware Embedding Memory for Continual Learning on Expanding Networks

Reference 2018

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:00:26.319897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5766e014-b208-498a-bfd8-38b032fbccc0 · outbound

This paper cites Praga: Prototype-aware graph adaptive aggregation for spatial multi-modal omics anal- ysis.

GCAL: Adapting Graph Models to Evolving Domain Shifts Praga: Prototype-aware graph adaptive aggregation for spatial multi-modal omics anal- ysis

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:00:29.534920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 06f2c109-52b6-4e19-a173-c487950ec8cf · outbound

This paper cites Graph Condensation via Receptive Field Distribution Matching.

GCAL: Adapting Graph Models to Evolving Domain Shifts Graph Condensation via Receptive Field Distribution Matching

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T15:00:24.048188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3cd9b77-2af3-439b-8bd2-fbb95ba92936 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

GCAL: Adapting Graph Models to Evolving Domain Shifts Semi-Supervised Classification with Graph Convolutional Networks

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T15:00:23.926486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e8558f8-137a-45a1-97d3-d0e667fd75e6 · outbound

This paper cites Adaptive path-memory network for tempo- ral knowledge graph reasoning.

GCAL: Adapting Graph Models to Evolving Domain Shifts Adaptive path-memory network for tempo- ral knowledge graph reasoning

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:00:29.970453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 64ce550f-a974-446e-a706-8f633905e4ab · outbound

This paper cites Graph Condensation for Graph Neural Networks.

GCAL: Adapting Graph Models to Evolving Domain Shifts Graph Condensation for Graph Neural Networks

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T15:00:23.867413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 936666cf-1160-4807-9493-1080e1c39b78 · outbound

This paper cites an unresolved cited work.

GCAL: Adapting Graph Models to Evolving Domain Shifts Unresolved cited work

Reference 2025

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:00:27.905974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

Observation 8d92c7c6-0583-429b-81a9-b6fe94aa23c9 · inbound

Cross-Resolution Semantic Learning for Graph Domain Adaptation cites this paper.

Cross-Resolution Semantic Learning for Graph Domain Adaptation GCAL: Adapting Graph Models to Evolving Domain Shifts

Reference 102

Resolution: unresolved; no resolver link, observed 2026-08-03T08:31:25.511131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.