SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs | Paper Citation Record

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This paper cites These algorithms rely on a sufﬁcient number of labeled nodes provided to ensure desirable clas- siﬁcation accuracy.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs These algorithms rely on a sufﬁcient number of labeled nodes provided to ensure desirable clas- siﬁcation accuracy

Reference 1

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This paper cites an unresolved cited work.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Unresolved cited work

Reference 2

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This paper cites This offers an advantage that the graph embedding network and the discriminator can collaborate with each other to mutually strengthen their performance.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs This offers an advantage that the graph embedding network and the discriminator can collaborate with each other to mutually strengthen their performance

Reference 3

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This paper cites The rest of this article is organized as follows.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs The rest of this article is organized as follows

Reference 4

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SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Unresolved cited work

Reference 5

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SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Unresolved cited work

Reference 6

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SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Unresolved cited work

Reference 7

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Observation 359d437e-a67c-43cc-b67f-08c35ee20e86 · outbound

This paper cites ANRMAB improves AGE by dynamically adjusting the weights of different strategies based on the MAB reward.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs ANRMAB improves AGE by dynamically adjusting the weights of different strategies based on the MAB reward

Reference 8

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Observation 07bcb08e-2051-4e54-99f4-948c51707056 · outbound

This paper cites This method is used to evaluate the advantages of GNN-based AL methods over traditional graph-based AL methods.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs This method is used to evaluate the advantages of GNN-based AL methods over traditional graph-based AL methods

Reference 9

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This paper cites To assess the importance of different aspects of SEAL, we also compare with four variants of SEAL via ablation studies.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs To assess the importance of different aspects of SEAL, we also compare with four variants of SEAL via ablation studies

Reference 10

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This paper cites Speciﬁcally, it changes G(·)’s loss function in (5) as J G = JGCN.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Speciﬁcally, it changes G(·)’s loss function in (5) as J G = JGCN

Reference 11

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Observation 88ce17c4-3b68-4f9a-ac83-34a8694206c1 · outbound

This paper cites an unresolved cited work.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Unresolved cited work

Reference 12

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Observation d7782646-dd4d-4bd6-9700-5721019c3c69 · outbound

This paper cites It is equivalent to setting α a s0i n( 8 ) , while other parameters remain the same as with SEAL.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs It is equivalent to setting α a s0i n( 8 ) , while other parameters remain the same as with SEAL

Reference 13

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This paper cites This is equiv- alent to setting δ to 1 in (6) and (7).

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs This is equiv- alent to setting δ to 1 in (6) and (7)

Reference 14

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Observation 9c5c21bb-d1a0-43af-8808-10b5754c5e3b · outbound

This paper cites Semi-supervised classiﬁcation with graph convolutional networks,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Semi-supervised classiﬁcation with graph convolutional networks,

Reference 15

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Observation 47561846-303d-4ca6-8828-b41a42d2d86a · outbound

This paper cites Active learning with extremely sparse labeled examples,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Active learning with extremely sparse labeled examples,

Reference 16

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Observation 833560a3-f0f6-406b-9fbe-f4df8435db4b · outbound

This paper cites Learning and inference with constraints,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Learning and inference with constraints,

Reference 17

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Observation 4c4c2ff3-b3b6-41da-8917-b58eaf32dc1b · outbound

This paper cites Heterogeneous uncertainty sampling for supervised learning,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Heterogeneous uncertainty sampling for supervised learning,

Reference 18

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Observation b6849d29-b05a-4f95-8d3a-58b979e4f9b9 · outbound

This paper cites Toward optimal active learning through monte carlo estimation of error reduction,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Toward optimal active learning through monte carlo estimation of error reduction,

Reference 19

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This paper cites Query by committee,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Query by committee,

Reference 20

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Observation a2f5fe25-ccad-4d1a-802a-d2d7dc3777b6 · outbound

This paper cites Employing EM and pool- based active learning for text classiﬁcation,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Employing EM and pool- based active learning for text classiﬁcation,

Reference 21

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This paper cites A sequential algorithm for training text classiﬁers,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs A sequential algorithm for training text classiﬁers,

Reference 22

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Observation a788b08c-6ba5-4920-b451-afa8fa00e186 · outbound

This paper cites A variance minimization criterion to active learning on graphs,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs A variance minimization criterion to active learning on graphs,

Reference 23

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Observation b978ed4d-6cc8-4038-adab-270ccf0d6627 · outbound

This paper cites Towards active learning on graphs: An error bound minimization approach,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Towards active learning on graphs: An error bound minimization approach,

Reference 24

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Observation dfd34874-5350-4113-a945-6d456a0a9432 · outbound

This paper cites σ -optimality for active learning on Gaussian random ﬁelds,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs σ -optimality for active learning on Gaussian random ﬁelds,

Reference 25

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Observation ae779cce-3687-4752-9517-39585cefa959 · outbound

This paper cites Active learning for networked data,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Active learning for networked data,

Reference 26

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Observation e8ef8324-478f-49d1-8a34-6a315be64bb4 · outbound

This paper cites Active class discovery and learning for networked data,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Active class discovery and learning for networked data,

Reference 27

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Observation 18403ef5-d1dd-4a95-afbf-21d7ddb82fa5 · outbound

This paper cites Active sampling for graph-aware classiﬁcation,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Active sampling for graph-aware classiﬁcation,

Reference 28

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Observation 09343d5e-bf7c-4747-860a-07b03df28d03 · outbound

This paper cites Graph Attention Networks.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Graph Attention Networks

Reference 29

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This paper cites Deep attributed net- work embedding by preserving structure and attribute information,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Deep attributed net- work embedding by preserving structure and attribute information,

Reference 30

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This paper cites Link-based classiﬁcation,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Link-based classiﬁcation,

Reference 31

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This paper cites Deepwalk: Online learning of social representations,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Deepwalk: Online learning of social representations,

Reference 32

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This paper cites Active Learning for Graph Embedding.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Active Learning for Graph Embedding

Reference 33

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This paper cites Active discriminative network representation learning,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Active discriminative network representation learning,

Reference 34

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This paper cites Multiple-instance active learning,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Multiple-instance active learning,

Reference 35

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This paper cites Scalable active learning for multiclass image classiﬁcation,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Scalable active learning for multiclass image classiﬁcation,

Reference 36

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This paper cites Neural networks and the bias/variance dilemma,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Neural networks and the bias/variance dilemma,

Reference 37

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This paper cites Information-ba sed objective functions for active data selection,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Information-ba sed objective functions for active data selection,

Reference 38

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This paper cites An analysis of active learning strategies for sequence labeling tasks,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs An analysis of active learning strategies for sequence labeling tasks,

Reference 39

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This paper cites Selective sampling for example-based word sense disambiguation,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Selective sampling for example-based word sense disambiguation,

Reference 40

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This paper cites Active learning literature survey,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Active learning literature survey,

Reference 41

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This paper cites an unresolved cited work.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Unresolved cited work

Reference 42

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This paper cites Batch mode active learning for networked data,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Batch mode active learning for networked data,

Reference 43

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This paper cites Active Semi-Supervised Learning using Submodular Functions.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Active Semi-Supervised Learning using Submodular Functions

Reference 44

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This paper cites Label selection on graphs,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Label selection on graphs,

Reference 45

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This paper cites Graph-based active learning based on label propagation,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Graph-based active learning based on label propagation,

Reference 46

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This paper cites A scalable algorithm for graph- based active learning,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs A scalable algorithm for graph- based active learning,

Reference 47

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This paper cites Combining active learning and semi-supervised learning using Gau ssian ﬁelds and harmonic functions,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Combining active learning and semi-supervised learning using Gau ssian ﬁelds and harmonic functions,

Reference 48

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Observation 7146093b-fc56-486d-9cda-d8b877dfbd60 · outbound

This paper cites Data-adaptive active sampling for efﬁcient graph-cognizant classiﬁcation,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Data-adaptive active sampling for efﬁcient graph-cognizant classiﬁcation,

Reference 49

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This paper cites Combining link and content for col- lective active learning,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Combining link and content for col- lective active learning,

Reference 50

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Observation e26d3663-cd68-414c-8838-b12bec7aad41 · outbound

This paper cites A survey on instance selection for active learning,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs A survey on instance selection for active learning,

Reference 51

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Observation b540317c-421a-41e1-a3c0-546498a2eb7c · outbound

This paper cites Generative adversarial nets,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Generative adversarial nets,

Reference 52

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Observation 645d5b69-87a2-476f-af45-af1fe17f4ff5 · outbound

This paper cites Adversarial active learning for sequences labeling and generation,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Adversarial active learning for sequences labeling and generation,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:52:33.969078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9eacc154-d660-429f-ba61-ffb2408f14ee · outbound

This paper cites V ariational adversarial active learning,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs V ariational adversarial active learning,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:52:33.953653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation aeb94cf0-bfac-4392-8e00-7cf97bd4fd51 · outbound

This paper cites Improved techniques for training GANs,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Improved techniques for training GANs,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:52:33.936785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2b5a72d1-d11c-4027-9d16-f2799f09513d · outbound

This paper cites Semi- supervised learning based on generative adversarial network: A com- parison between good GAN and bad GAN approach,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Semi- supervised learning based on generative adversarial network: A com- parison between good GAN and bad GAN approach,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:52:33.921771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ff3af24a-aef0-43ad-a628-965aecc1716a · outbound

This paper cites Towards Principled Unsupervised Learning.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Towards Principled Unsupervised Learning

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-14T11:52:33.645310Z

Source-reported events for the cited work

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Observation 3631b617-936c-4424-87be-a4ab29832082 · outbound

This paper cites Good semi-supervised learning that requires a bad GAN,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Good semi-supervised learning that requires a bad GAN,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:52:33.906045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 41788fda-253c-422c-a2e8-75635ad4da49 · outbound

This paper cites Collective classiﬁcation in network data,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Collective classiﬁcation in network data,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:52:33.890663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ddd60a4f-dc8c-4827-aac6-df1dd7614b9f · outbound

This paper cites Attributed network embedding via subspace discovery,.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs Attributed network embedding via subspace discovery,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:52:33.875077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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