MADE: Graph Backdoor Defense with Masked Unlearning | Paper Citation Record

Observation 7485f764-06f4-4d73-9bbc-25c6ce4dcc5d · outbound

This paper cites Graph neural networks for social recommendation,.

MADE: Graph Backdoor Defense with Masked Unlearning Graph neural networks for social recommendation,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:10.387173Z

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Observation 3592f5eb-80fc-4b98-9a12-903285058d8d · outbound

This paper cites Random graph models of social networks,.

MADE: Graph Backdoor Defense with Masked Unlearning Random graph models of social networks,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:10.373485Z

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 90b56844-6987-48ee-aaf5-27db9c96bbed · outbound

This paper cites Trinajstic, Chemical graph theory.

MADE: Graph Backdoor Defense with Masked Unlearning Trinajstic, Chemical graph theory

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:10.359605Z

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 315ab3f8-a176-44fd-9e74-320d114b6108 · outbound

This paper cites Beyond homophily in graph neural networks: Current limitations and effective designs,.

MADE: Graph Backdoor Defense with Masked Unlearning Beyond homophily in graph neural networks: Current limitations and effective designs,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:10.345730Z

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 5197be1e-14c4-4f3e-8646-54c357756bf3 · outbound

This paper cites Powerful graph convolutional networks with adaptive propagation mechanism for homophily and heterophily,.

MADE: Graph Backdoor Defense with Masked Unlearning Powerful graph convolutional networks with adaptive propagation mechanism for homophily and heterophily,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:10.331556Z

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 f27110b5-5bcc-401a-9a0d-5659c0afe2d2 · outbound

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

MADE: Graph Backdoor Defense with Masked Unlearning Semi-Supervised Classification with Graph Convolutional Networks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T11:48:09.328783Z

Source-reported events for the cited work

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Observation 3d99f80c-faa0-4fdf-98c7-a718e040f0d4 · outbound

This paper cites Inductive representation learning on large graphs,.

MADE: Graph Backdoor Defense with Masked Unlearning Inductive representation learning on large graphs,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T11:48:09.334101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 627694c8-c0aa-440a-ade1-35a28106455a · outbound

This paper cites Gated graph sequence neural networks,.

MADE: Graph Backdoor Defense with Masked Unlearning Gated graph sequence neural networks,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:10.307508Z

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

Observation 2c770ef9-cdbe-4a83-9684-e32f6f998387 · outbound

This paper cites Predicting drug–target interaction using a novel graph neural network with 3d structure-embedded graph representation,.

MADE: Graph Backdoor Defense with Masked Unlearning Predicting drug–target interaction using a novel graph neural network with 3d structure-embedded graph representation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:10.293617Z

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

Observation ffe35faa-2c8c-4f66-82f3-be5a170a3520 · outbound

This paper cites Kgnn: Knowledge graph neural network for drug-drug interaction prediction.

MADE: Graph Backdoor Defense with Masked Unlearning Kgnn: Knowledge graph neural network for drug-drug interaction prediction

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:10.279363Z

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 633a7e61-ac37-440d-a759-0e16316084cf · outbound

This paper cites Could graph neural networks learn better molecular representation for drug discovery? a comparison study of descriptor-based and graph-based models,.

MADE: Graph Backdoor Defense with Masked Unlearning Could graph neural networks learn better molecular representation for drug discovery? a comparison study of descriptor-based and graph-based models,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:10.265143Z

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 95bec72d-59eb-4ad5-ac9e-09ae9b1c87a6 · outbound

This paper cites Graph neural network for traffic forecasting: A survey,.

MADE: Graph Backdoor Defense with Masked Unlearning Graph neural network for traffic forecasting: A survey,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:10.251041Z

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 b272f3ca-fe82-4a2e-97db-2cdb2f018d78 · outbound

This paper cites Point-gnn: Graph neural network for 3d object detection in a point cloud,.

MADE: Graph Backdoor Defense with Masked Unlearning Point-gnn: Graph neural network for 3d object detection in a point cloud,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:10.237081Z

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 a9dac2d9-94ae-4da7-8844-c7a00cff9cc5 · outbound

This paper cites Neural graph collaborative filtering,.

MADE: Graph Backdoor Defense with Masked Unlearning Neural graph collaborative filtering,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:10.223544Z

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 5ea2f21d-3674-490c-a62d-eca1933e6311 · outbound

This paper cites Scaling graph neural networks with approxi- mate pagerank,.

MADE: Graph Backdoor Defense with Masked Unlearning Scaling graph neural networks with approxi- mate pagerank,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:10.209031Z

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 cdb881e4-ece8-4679-9f83-30728c9d9aac · outbound

This paper cites Predict then Propagate: Graph Neural Networks meet Personalized PageRank.

MADE: Graph Backdoor Defense with Masked Unlearning Predict then Propagate: Graph Neural Networks meet Personalized PageRank

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T11:48:09.373547Z

Source-reported events for the cited work

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Observation 7a382b2d-d580-46fb-8215-ea15e39b95b9 · outbound

This paper cites Graph backdoor,.

MADE: Graph Backdoor Defense with Masked Unlearning Graph backdoor,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:10.193153Z

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 24976171-aebd-417a-b7ea-48c509edcd4b · outbound

This paper cites Backdoor attacks to graph neural networks,.

MADE: Graph Backdoor Defense with Masked Unlearning Backdoor attacks to graph neural networks,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:10.178936Z

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 b1200a99-b4ae-4b61-841d-0b156d0707c7 · outbound

This paper cites Transferable graph backdoor attack,.

MADE: Graph Backdoor Defense with Masked Unlearning Transferable graph backdoor attack,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:10.164148Z

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 0a77a778-5674-48ed-8ba5-3aaf82b3eaee · outbound

This paper cites Adversarial neuron pruning purifies backdoored deep models,.

MADE: Graph Backdoor Defense with Masked Unlearning Adversarial neuron pruning purifies backdoored deep models,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:10.149554Z

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 5f0a78dc-5741-4199-ad41-9ab2e9c77006 · outbound

This paper cites Anti- backdoor learning: Training clean models on poisoned data,.

MADE: Graph Backdoor Defense with Masked Unlearning Anti- backdoor learning: Training clean models on poisoned data,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:10.135419Z

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 3bda1cc4-d3d1-44cf-b2b0-2eab37928c83 · outbound

This paper cites Fine-Tuning Is All You Need to Mitigate Backdoor Attacks.

MADE: Graph Backdoor Defense with Masked Unlearning Fine-Tuning Is All You Need to Mitigate Backdoor Attacks

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T11:48:09.400727Z

Source-reported events for the cited work

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Observation 1f0640cd-0178-490c-a9ab-dfa0dba50722 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

MADE: Graph Backdoor Defense with Masked Unlearning Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T11:48:09.405469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fa5f7e3-ca9d-4d04-9532-b308ea7426a5 · outbound

This paper cites Deep residual learning for image recognition,.

MADE: Graph Backdoor Defense with Masked Unlearning Deep residual learning for image recognition,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T11:48:09.410415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 299a7c7d-a6b5-4405-a97d-fe35652c0df6 · outbound

This paper cites Attention is all you need,.

MADE: Graph Backdoor Defense with Masked Unlearning Attention is all you need,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T11:48:09.414574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cebfe35c-1d34-4079-b531-09b7d2ef5877 · outbound

This paper cites Spectral signatures in backdoor attacks,.

MADE: Graph Backdoor Defense with Masked Unlearning Spectral signatures in backdoor attacks,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:10.102032Z

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 d6c41fb2-b5f5-4883-87bc-1c8e716ad642 · outbound

This paper cites Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning.

MADE: Graph Backdoor Defense with Masked Unlearning Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T11:48:09.422784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2c95cc2-430a-457c-8f1a-5c843b53c272 · outbound

This paper cites BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain.

MADE: Graph Backdoor Defense with Masked Unlearning BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T11:48:09.428229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bf00900-bbc4-4084-ac14-12806369dc64 · outbound

This paper cites Reflection backdoor: A natural backdoor attack on deep neural networks,.

MADE: Graph Backdoor Defense with Masked Unlearning Reflection backdoor: A natural backdoor attack on deep neural networks,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:10.087806Z

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 0f7d6d8c-1da4-438d-9ad5-df652220cb3a · outbound

This paper cites Re- thinking the reverse-engineering of trojan triggers,.

MADE: Graph Backdoor Defense with Masked Unlearning Re- thinking the reverse-engineering of trojan triggers,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:10.073412Z

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 8fc8d755-3132-4057-a19e-76acc99b2e04 · outbound

This paper cites Few-shot backdoor defense using shapley estimation,.

MADE: Graph Backdoor Defense with Masked Unlearning Few-shot backdoor defense using shapley estimation,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:10.058345Z

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 1a981ac3-9aec-4644-a8e1-0d5a6644d0dd · outbound

This paper cites Unnotice- able backdoor attacks on graph neural networks,.

MADE: Graph Backdoor Defense with Masked Unlearning Unnotice- able backdoor attacks on graph neural networks,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:10.044211Z

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 3897a9a8-de8a-431d-8a98-292098bf4070 · outbound

This paper cites Textual backdoor attack for the text classification system,.

MADE: Graph Backdoor Defense with Masked Unlearning Textual backdoor attack for the text classification system,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:10.030581Z

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 e0ee6e92-964a-44ed-8a57-b5af50e3d613 · outbound

This paper cites Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer.

MADE: Graph Backdoor Defense with Masked Unlearning Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-12T11:48:09.453816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7e1401c-8d6c-4961-877f-6537396f7855 · outbound

This paper cites A backdoor attack against lstm-based text classification systems,.

MADE: Graph Backdoor Defense with Masked Unlearning A backdoor attack against lstm-based text classification systems,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:10.015965Z

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 7aadce62-d3d1-4bb5-81df-7a8087c8b410 · outbound

This paper cites How does heterophily impact the robustness of graph neural networks? theoretical connections and practical implications,.

MADE: Graph Backdoor Defense with Masked Unlearning How does heterophily impact the robustness of graph neural networks? theoretical connections and practical implications,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:10.000280Z

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 bd2d5980-58da-483a-b78c-8ec28148c055 · outbound

This paper cites Finding global homophily in graph neural networks when meeting heterophily,.

MADE: Graph Backdoor Defense with Masked Unlearning Finding global homophily in graph neural networks when meeting heterophily,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:09.985963Z

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 639457db-30ee-40c8-be47-e0472f2d8be7 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization,.

MADE: Graph Backdoor Defense with Masked Unlearning Grad-cam: Visual explanations from deep networks via gradient-based localization,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:09.971886Z

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 85587266-037b-41ef-b1ad-ed11b470026a · outbound

This paper cites Debiasing Backdoor Attack: A Benign Application of Backdoor Attack in Eliminating Data Bias.

MADE: Graph Backdoor Defense with Masked Unlearning Debiasing Backdoor Attack: A Benign Application of Backdoor Attack in Eliminating Data Bias

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-12T11:48:09.670096Z

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 fc9d4e3f-ccd1-4ca8-94c4-7955d3c9ce5b · outbound

This paper cites Iam graph database reposi- tory for graph based pattern recognition and machine learning,.

MADE: Graph Backdoor Defense with Masked Unlearning Iam graph database reposi- tory for graph based pattern recognition and machine learning,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:09.957849Z

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 fc33b25d-ba7a-4523-96b7-0da3ec5d5c31 · outbound

This paper cites Protein function prediction via graph kernels,.

MADE: Graph Backdoor Defense with Masked Unlearning Protein function prediction via graph kernels,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:09.943629Z

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 85d69dc5-dc92-4da4-881c-aceba0662f55 · outbound

This paper cites Automating the construction of internet portals with machine learning,.

MADE: Graph Backdoor Defense with Masked Unlearning Automating the construction of internet portals with machine learning,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-12T11:48:09.488440Z

Source-reported events for the cited work

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Observation 745abff6-65ad-4c80-b622-0520aa92747e · outbound

This paper cites Collective classification in network data,.

MADE: Graph Backdoor Defense with Masked Unlearning Collective classification in network data,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:09.920203Z

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 9d3861a7-c558-4c13-9035-b89b4ad9091c · outbound

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

MADE: Graph Backdoor Defense with Masked Unlearning Open graph benchmark: Datasets for machine learning on graphs,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:09.905626Z

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 d6eeae10-ca14-44fc-82a6-ca82ef42c892 · outbound

This paper cites GraphSAINT: Graph Sampling Based Inductive Learning Method.

MADE: Graph Backdoor Defense with Masked Unlearning GraphSAINT: Graph Sampling Based Inductive Learning Method

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-12T11:48:09.501189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 292197e0-1a41-4c29-b279-e26e29c36dfc · outbound

This paper cites Graph informa- tion bottleneck,.

MADE: Graph Backdoor Defense with Masked Unlearning Graph informa- tion bottleneck,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:09.890886Z

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 94f8c77a-f4d7-4f86-a0fa-bd1a1e393343 · outbound

This paper cites Graph Attention Networks.

MADE: Graph Backdoor Defense with Masked Unlearning Graph Attention Networks

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-12T11:48:09.509714Z

Source-reported events for the cited work

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Observation d82b4d8d-374a-4e84-8b27-50f6a59a38ec · outbound

This paper cites Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation.

MADE: Graph Backdoor Defense with Masked Unlearning Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-12T11:48:09.514129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65bad140-d3f6-4300-8e4b-ca2f3e8183bb · outbound

This paper cites Clean-label backdoor attacks on video recognition models,.

MADE: Graph Backdoor Defense with Masked Unlearning Clean-label backdoor attacks on video recognition models,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:09.875993Z

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 1440e17b-67f1-4c5f-b6c5-9c2d135720e1 · outbound

This paper cites Backdoor Defense via Decoupling the Training Process.

MADE: Graph Backdoor Defense with Masked Unlearning Backdoor Defense via Decoupling the Training Process

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-12T11:48:09.522831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79e395ad-2347-48cf-9661-f0bccdbb6e99 · outbound

This paper cites Defending Against Backdoor Attack on Graph Nerual Network by Explainability.

MADE: Graph Backdoor Defense with Masked Unlearning Defending Against Backdoor Attack on Graph Nerual Network by Explainability

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-12T11:48:09.527428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c77ebb2-2302-42d5-95b7-c373cb296d77 · outbound

This paper cites Svd-gcn: A simpli- fied graph convolution paradigm for recommendation,.

MADE: Graph Backdoor Defense with Masked Unlearning Svd-gcn: A simpli- fied graph convolution paradigm for recommendation,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:09.860865Z

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 9eff147a-48c5-4e6e-bea4-c8f32cfe367a · outbound

This paper cites In this paper, the experiment JOURNAL OF LATEX CLASS FILES, VOL.

MADE: Graph Backdoor Defense with Masked Unlearning In this paper, the experiment JOURNAL OF LATEX CLASS FILES, VOL

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:09.844923Z

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 13456e22-de08-4569-974a-2bc8a0b361dd · outbound

This paper cites an unresolved cited work.

MADE: Graph Backdoor Defense with Masked Unlearning Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-12T11:48:09.829579Z

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 146ed87f-c684-447d-994e-191c3c603bc5 · outbound

This paper cites For typical graph defense methods,.

MADE: Graph Backdoor Defense with Masked Unlearning For typical graph defense methods,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:09.815073Z

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 7596040f-fc68-4881-8a53-967310863afe · outbound

This paper cites an unresolved cited work.

MADE: Graph Backdoor Defense with Masked Unlearning Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-12T11:48:09.800475Z

Source-reported events for the cited work

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Observation 6c17c3b3-fba9-474b-8eba-cf64cb53e313 · outbound

This paper cites This modification aims to concentrate solely on the features associated with the K-largest singular vectors, thereby enhancing the model’s robustness to perturbations.

MADE: Graph Backdoor Defense with Masked Unlearning This modification aims to concentrate solely on the features associated with the K-largest singular vectors, thereby enhancing the model’s robustness to perturbations

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:48:09.785514Z

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

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