Paper Citation Record · LEDGER

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning

As of 22 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2505.10040.

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

pith.paper-citation-record.v1
2505.10040 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:21:12.762470Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

45 of 45 outbound references displayed

verified exact0
verified fuzzy20
unresolved25
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3d5aa948-30d2-4d33-aec6-246e1da1c0c4 · outbound

This paper cites Memory aware synapses: Learning what (not) to forget.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Memory aware synapses: Learning what (not) to forget

Reference 1

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Source-reported events for the cited work

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Observation 719322c6-8859-4df9-a68d-383d85cbee0a · outbound

This paper cites Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T21:21:12.598080Z

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Observation 7222c57c-1e03-4344-a166-9a4b76e4ed4f · outbound

This paper cites Mm-gnn: Mix-moment graph neural network towards modeling neighborhood feature distribution.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Mm-gnn: Mix-moment graph neural network towards modeling neighborhood feature distribution

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:21:13.288207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5285d065-3029-4e3f-9785-6229a1ea6cf3 · outbound

This paper cites Efficient statistical sampling adaptation for exemplar-free class incremental learning.IEEE Transactions on Circuits and Systems for Video Technology, 2024.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Efficient statistical sampling adaptation for exemplar-free class incremental learning.IEEE Transactions on Circuits and Systems for Video Technology, 2024

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:21:13.276509Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 42e1f8e7-462f-4e79-838a-ecf6de3fe5a1 · outbound

This paper cites Protognn: Prototype-assisted message passing framework for non-homophilous graphs.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Protognn: Prototype-assisted message passing framework for non-homophilous graphs

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:21:13.264268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f10cadb2-7dd6-4c14-8e62-88fae506102e · outbound

This paper cites Exemplar-free continual representation learning via learnable drift compensation.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Exemplar-free continual representation learning via learnable drift compensation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:21:13.250253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a1c8012f-25fb-4477-bdc2-910ae88735b2 · outbound

This paper cites Inductive representation learning on large graphs.Advances in neural information processing systems, 30, 2017.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Inductive representation learning on large graphs.Advances in neural information processing systems, 30, 2017

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T21:21:12.619300Z

Source-reported events for the cited work

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Observation 9f62bb15-545b-4479-a163-4defe8c2f606 · outbound

This paper cites Contrastive multi-view representation learning on graphs.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Contrastive multi-view representation learning on graphs

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:21:13.223134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 04f6eb88-0ee3-43f0-94d5-c80b861a97ef · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Distilling the Knowledge in a Neural Network

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T21:21:12.626862Z

Source-reported events for the cited work

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Observation 8b184e42-d8eb-4423-b7a9-ef6cd4b397ec · outbound

This paper cites Open graph benchmark: Datasets for machine learning on graphs.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Open graph benchmark: Datasets for machine learning on graphs

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T21:21:12.630787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0bf674e8-101a-4a15-abf1-8e3a64d035f6 · outbound

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

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Semi-Supervised Classification with Graph Convolutional Networks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T21:21:12.634571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c4d9aa2-88cd-4845-83d3-c33e8c952fda · outbound

This paper cites Overcoming catastrophic forgetting in neural networks.Proceedings of the national academy of sciences, 114(13):3521–3526, 2017.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Overcoming catastrophic forgetting in neural networks.Proceedings of the national academy of sciences, 114(13):3521–3526, 2017

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T21:21:12.638914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2766259-17fd-45cd-8e96-d3c62a54b9f0 · outbound

This paper cites Fcs: Feature calibration and separation for non- exemplar class incremental learning.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Fcs: Feature calibration and separation for non- exemplar class incremental learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:21:13.194649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bbd76f73-5a2c-4d06-a776-657b05646ae4 · outbound

This paper cites Inductive graph few-shot class incremental learning.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Inductive graph few-shot class incremental learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:21:13.182373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation abc9678c-49f1-4349-9e10-84b60346768c · outbound

This paper cites Learning without forgetting.IEEE transactions on pattern analysis and machine intelligence, 40(12):2935–2947, 2017.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Learning without forgetting.IEEE transactions on pattern analysis and machine intelligence, 40(12):2935–2947, 2017

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T21:21:12.650414Z

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Observation 8ae59778-93d6-4540-a431-a4886888208f · outbound

This paper cites Overcoming catastrophic forgetting in graph neural networks.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Overcoming catastrophic forgetting in graph neural networks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T21:21:12.654143Z

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Observation caee6416-bef6-469f-a877-2feec7578d7e · outbound

This paper cites Cat: Balanced continual graph learning with graph condensation.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Cat: Balanced continual graph learning with graph condensation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T21:21:12.657878Z

Source-reported events for the cited work

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Observation a1f78f84-5327-43c4-9177-0c845c8788bc · outbound

This paper cites Gradient episodic memory for continual learning.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Gradient episodic memory for continual learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T21:21:12.661797Z

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Observation 429fca47-54b5-44a2-93c4-03fee5d9901c · outbound

This paper cites Elastic Feature Consolidation for Cold Start Exemplar-Free Incremental Learning.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Elastic Feature Consolidation for Cold Start Exemplar-Free Incremental Learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T21:21:12.665498Z

Source-reported events for the cited work

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Observation 2f1ee6b4-1d54-4b6c-ae13-1dbe917af221 · outbound

This paper cites Automating the construction of internet portals with machine learning.Information Retrieval, 3:127–163, 2000.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Automating the construction of internet portals with machine learning.Information Retrieval, 3:127–163, 2000

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:21:13.137480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7684b477-9ebe-4e18-a845-d8d8034cf6de · outbound

This paper cites Distributed repre- sentations of words and phrases and their compositionality.Advances in neural information processing systems, 26, 2013.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Distributed repre- sentations of words and phrases and their compositionality.Advances in neural information processing systems, 26, 2013

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T21:21:12.673650Z

Source-reported events for the cited work

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Observation fba58cba-b8d2-4aad-bd97-cd7951a694f3 · outbound

This paper cites Replay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting Approach.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Replay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting Approach

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T21:21:12.677418Z

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Observation 864f9992-9856-4cbc-87df-0c5914686b24 · outbound

This paper cites The pagerank citation ranking: Bringing order to the web.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning The pagerank citation ranking: Bringing order to the web

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T21:21:12.681568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53668f2d-2ccd-459c-a264-0e7733c98df6 · outbound

This paper cites Fetril: Feature translation for exemplar-free class-incremental learning.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Fetril: Feature translation for exemplar-free class-incremental learning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T21:21:12.685214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e9a3309-cec5-4bea-9f9f-7ce81a18a619 · outbound

This paper cites Incremental graph classification by class prototype construction and augmentation.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Incremental graph classification by class prototype construction and augmentation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:21:13.100978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ca6df8e8-ed08-487f-8001-ba5646ccb13d · outbound

This paper cites Graph neural networks: Architectures, stability, and transferability.Proceedings of the IEEE, 109(5):660–682, 2021.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Graph neural networks: Architectures, stability, and transferability.Proceedings of the IEEE, 109(5):660–682, 2021

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:21:13.088103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b687d5f4-9d4b-4e1c-bdf0-7b95889487c7 · outbound

This paper cites The lattice theory of information.Transactions of the IRE professional Group on Information Theory, 1(1):105–107, 1953.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning The lattice theory of information.Transactions of the IRE professional Group on Information Theory, 1(1):105–107, 1953

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:21:13.075406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d692aa43-5c14-4d44-b093-7285c0e33c0d · outbound

This paper cites A mathematical theory of communication.The Bell system technical journal, 27(3):379–423, 1948.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning A mathematical theory of communication.The Bell system technical journal, 27(3):379–423, 1948

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T21:21:12.700060Z

Source-reported events for the cited work

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Observation 5fa1a21b-f643-4f9b-8575-fa249845bab7 · outbound

This paper cites Pitfalls of Graph Neural Network Evaluation.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Pitfalls of Graph Neural Network Evaluation

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T21:21:12.704066Z

Source-reported events for the cited work

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Observation d03909fa-cdc1-4b87-a320-a85f471f47c3 · outbound

This paper cites Prototypical networks for few-shot learning.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Prototypical networks for few-shot learning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T21:21:12.707960Z

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Observation 0aa6a432-df9a-44a9-8ab2-1e3f71073065 · outbound

This paper cites Graph Attention Networks.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Graph Attention Networks

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T21:21:12.711725Z

Source-reported events for the cited work

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Observation 4691901a-70f3-424d-b32b-36fe568be063 · outbound

This paper cites Non-exemplar class- incremental learning via adaptive old class reconstruction.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Non-exemplar class- incremental learning via adaptive old class reconstruction

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:21:13.046201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c82ac3e0-bff7-4f00-a02c-e6921b9b6d86 · outbound

This paper cites Mixup for node and graph classification.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Mixup for node and graph classification

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:21:13.032981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cecb23b6-9e7c-4a2b-a546-7cb96c8a750e · outbound

This paper cites Simplifying graph convolutional networks.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Simplifying graph convolutional networks

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:21:13.021579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1bdafa59-fdf7-40a8-bcfb-acf25b03d9cc · outbound

This paper cites Infogcl: Information-aware graph contrastive learning.Advances in Neural Information Processing Systems, 34:30414–30425, 2021.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Infogcl: Information-aware graph contrastive learning.Advances in Neural Information Processing Systems, 34:30414–30425, 2021

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:21:13.009301Z

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Observation 3ddc2729-1f84-42e9-b3b3-89ea433a8554 · outbound

This paper cites How Powerful are Graph Neural Networks?.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning How Powerful are Graph Neural Networks?

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T21:21:12.728502Z

Source-reported events for the cited work

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Observation bf8c4c3a-1e52-49d8-a852-0a4d292112de · outbound

This paper cites Semantic drift compensation for class-incremental learning.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Semantic drift compensation for class-incremental learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:21:12.996479Z

Source-reported events for the cited work

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Observation 9489b169-008f-4833-bbde-39f3693a4e0c · outbound

This paper cites Continual learning through synaptic intelligence.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Continual learning through synaptic intelligence

Reference 38

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Source-reported events for the cited work

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Observation 34a38916-818b-4be3-aa95-29c2ca906e05 · outbound

This paper cites Fine-grained knowledge selection and restoration for non-exemplar class incremental learning.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Fine-grained knowledge selection and restoration for non-exemplar class incremental learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:21:12.975994Z

Source-reported events for the cited work

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Observation b57674b5-a112-4b06-a974-688ba7d013f4 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning mixup: Beyond Empirical Risk Minimization

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T21:21:12.742146Z

Source-reported events for the cited work

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Observation 5f81f228-0270-49d6-bd6b-f34f4f53160f · outbound

This paper cites Continual learning on dynamic graphs via parameter isolation.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Continual learning on dynamic graphs via parameter isolation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:21:12.963322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 31f1dd9c-cb59-4a23-9a5b-012419978d4d · outbound

This paper cites Cglb: Benchmark tasks for continual graph learning.Advances in Neural Information Processing Systems, 35:13006–13021, 2022.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Cglb: Benchmark tasks for continual graph learning.Advances in Neural Information Processing Systems, 35:13006–13021, 2022

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-15T21:21:12.749974Z

Source-reported events for the cited work

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Observation 299d6ac5-b1c9-46cb-8a61-7ce07410612a · outbound

This paper cites Sparsified subgraph memory for continual graph representation learning.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Sparsified subgraph memory for continual graph representation learning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:21:12.942616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 18b09835-d7e9-4d05-8cac-2cb2c7d27729 · outbound

This paper cites Overcoming catastrophic forgetting in graph neural networks with experience replay.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Overcoming catastrophic forgetting in graph neural networks with experience replay

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T21:21:12.758402Z

Source-reported events for the cited work

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Observation 139ad472-8de0-4e22-b87b-2e4df0320b70 · outbound

This paper cites Prototype augmentation and self-supervision for incremental learning.

Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning Prototype augmentation and self-supervision for incremental learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:21:12.920315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

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