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Paper Citation Record · LEDGER

Active Learning on Adversarially Corrupted Graphs

As of 7 August 2026, this Paper Citation Record lists 100 of 300 outbound references and 0 inbound Pith citation observations for arXiv:2607.04869.

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

pith.paper-citation-record.v1
2607.04869 v1

Coverage vector

measured 100 of 300 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T12:12:44.455418Z

measured 100 of 100 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 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

100 of 300 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved98
  • parse uncertain1
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4b82d6ba-9df4-4d32-bc26-716774fd85d6 · outbound

This paper cites Proceedings of the forty-second ACM symposium on Theory of computing , pages=.

Active Learning on Adversarially Corrupted Graphs Proceedings of the forty-second ACM symposium on Theory of computing , pages=

Reference 1

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:dfebaf851bebd8e532f4a214c52c135dbe28a3df90831946843ab41abc6e510c

Observation 19d116d9-466d-46a7-b5ed-9f23bc9a8586 · outbound

This paper cites Proceedings of the 55th Annual ACM Symposium on Theory of Computing , pages=.

Active Learning on Adversarially Corrupted Graphs Proceedings of the 55th Annual ACM Symposium on Theory of Computing , pages=

Reference 2

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:61c37c52c87626dd603891d4785984c745915831cb85e9a6dabbf60d3c80d896

Observation 3a778978-0eec-4ca7-b340-8a2900e1b72e · outbound

This paper cites Proceedings of the 56th Annual ACM Symposium on Theory of Computing , pages=.

Active Learning on Adversarially Corrupted Graphs Proceedings of the 56th Annual ACM Symposium on Theory of Computing , pages=

Reference 3

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:2949e4987608b318263082e499b8ad45dec027e623d6680daa13e9b17cf103ce

Observation 68595436-9103-4c19-a0e2-4e1720223fc6 · outbound

This paper cites Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) , pages=.

Active Learning on Adversarially Corrupted Graphs Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) , pages=

Reference 4

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:48ab46b8ee899a3d08e4444f4701be799327205b38328a8128aeb254e205e2a0

Observation 735a8cd4-1c2c-4d66-b714-e1bf263b8338 · outbound

This paper cites 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS) , pages=.

Active Learning on Adversarially Corrupted Graphs 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS) , pages=

Reference 5

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:3a7915b466a330ac2beaf799245df7d2ab52a4869db8f95c96cd6eafcdc1bb1b

Observation d042f033-6196-4509-86dc-4004d25a4ae7 · outbound

This paper cites Theory of Computing , volume=.

Active Learning on Adversarially Corrupted Graphs Theory of Computing , volume=

Reference 6

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:a9c9d4cf8f4861ea167f71f87744eb9b3f9b17d67b8a7099f87c67c6119652f0

Observation ad5e8502-1aab-4a38-8950-a0bb4363da07 · outbound

This paper cites arXiv preprint arXiv:2412.17115 , year=.

Active Learning on Adversarially Corrupted Graphs arXiv preprint arXiv:2412.17115 , year=

Reference 7

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:43fd41dcdf69e3fd6cfa52eaca6fc6e8c47382018e6362be780a19f68b4d14ef

Observation 1384e0ac-3566-4db5-bf36-f6fb7e83e5a6 · outbound

This paper cites Ergun and Zhili Feng and Sandeep Silwal and David P.

Active Learning on Adversarially Corrupted Graphs Ergun and Zhili Feng and Sandeep Silwal and David P

Reference 8

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:140af12d4a54aeb120d9066a06419e8692c66301df543ce226386b4ad7e9408b

Observation bcdd291e-c30b-43f7-9ecf-63b7bd076c1a · outbound

This paper cites Conference on Learning Theory , pages=.

Active Learning on Adversarially Corrupted Graphs Conference on Learning Theory , pages=

Reference 9

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:f51358c9b7d1ca50280678f96a46418d2065eee1a105b9b4981036f1cf0e79c4

Observation 38c7cb61-eae9-4a2b-9b3f-6b6d7b504e96 · outbound

This paper cites Journal of the ACM (JACM) , volume=.

Active Learning on Adversarially Corrupted Graphs Journal of the ACM (JACM) , volume=

Reference 10

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:9cffcb74e2490d0da0dbe251017aa47f442e722bc7a8d8b2dc0fb3d9cf507d8e

Observation 571d2b70-45a7-4785-b5db-ee0301072d44 · outbound

This paper cites Conference on Learning Theory , pages=.

Active Learning on Adversarially Corrupted Graphs Conference on Learning Theory , pages=

Reference 11

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:b66210b04f5eb568b8e04c064a52fd462933dcbc31aa76b4b87188b573d3eed9

Observation b6791e7b-1ae3-4f53-89e4-262ef0b44f22 · outbound

This paper cites Proceedings of The Web Conference 2020 , pages=.

Active Learning on Adversarially Corrupted Graphs Proceedings of The Web Conference 2020 , pages=

Reference 12

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:eda969046f640f587098066669dda8c78b83b26b79f63093f7e10547ff0f3e6c

Observation 59b6ade0-4a82-49eb-aa5a-7fdc556e0f45 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Active Learning on Adversarially Corrupted Graphs Advances in Neural Information Processing Systems , volume=

Reference 13

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:79f13d1a2a4b9ed82b19ccdafe2a51be1bbb163971a27b0eb648e32f25472370

Observation ffba6ca0-ff4a-4c9a-9c0f-2b09bcc4587b · outbound

This paper cites 9th Innovations in Theoretical Computer Science Conference (ITCS 2018) , year=.

Active Learning on Adversarially Corrupted Graphs 9th Innovations in Theoretical Computer Science Conference (ITCS 2018) , year=

Reference 14

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:3eab9b760fd3ed122448b37967d03268055df2c2f1adfc4282a7f946efb3413b

Observation c00ec95f-f673-4295-bf9e-6132ceedb50e · outbound

This paper cites Advances in neural information processing systems , volume=.

Active Learning on Adversarially Corrupted Graphs Advances in neural information processing systems , volume=

Reference 15

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:67226fffd9657ffcc476754854e9ec383180092f1a3a6cdc3579918427dbe26a

Observation 9720d610-72a3-4b77-a3ce-f6571d518b54 · outbound

This paper cites Communications of the ACM , volume=.

Active Learning on Adversarially Corrupted Graphs Communications of the ACM , volume=

Reference 16

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:ae5839f1660cd0aa3e5c5200a62701441f0077b6a65b580876a11c0211044c09

Observation ea6d9d4d-7a20-46b1-a071-94247d6108a6 · outbound

This paper cites Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete algorithms , pages=.

Active Learning on Adversarially Corrupted Graphs Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete algorithms , pages=

Reference 17

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:033f458d9d8fffaf0da53157e5d8a5b1ce03867564935c9e1cd9e2ba24fc5988

Observation 40e85649-4be7-4ee4-b144-d1ae757239bc · outbound

This paper cites 2012 , publisher=.

Active Learning on Adversarially Corrupted Graphs 2012 , publisher=

Reference 18

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:f3e4fac9a1deca4708f4602caa8d0de1c26c230fb49d2e727d8ab71cb60e60af

Observation 3f25e5de-61d4-4bff-b58d-f21750889acd · outbound

This paper cites Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) , pages=.

Active Learning on Adversarially Corrupted Graphs Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) , pages=

Reference 19

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:d0ad09177c8a8596f3d7fa26d85f3531010bc3980e1374c114ecb65585d0b956

Observation 929cebff-3097-473b-a2a2-6d25021d40a6 · outbound

This paper cites Outlier-robust moment-estimation via sum-of-squares.

Active Learning on Adversarially Corrupted Graphs Outlier-robust moment-estimation via sum-of-squares

Reference 20

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:ab19b6bde91257d182b53549b075fdd6f9b9df8b0f225ec380fa5fa1e1c37c15

Observation 6b4369cb-3cca-48a5-bc8e-e5f88f5c681c · outbound

This paper cites SIAM Journal on Computing , volume=.

Active Learning on Adversarially Corrupted Graphs SIAM Journal on Computing , volume=

Reference 21

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:1a02d877233187baec20ae9af2da90df21ee7b69d90c5cd2ba066fc0125b32ad

Observation 0e25df63-40c3-40ad-94b8-1a573e904ab4 · outbound

This paper cites 22nd Annual Symposium on Foundations of Computer Science (sfcs 1981) , pages=.

Active Learning on Adversarially Corrupted Graphs 22nd Annual Symposium on Foundations of Computer Science (sfcs 1981) , pages=

Reference 22

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:ff38bc6aef9feb42e9319b8b1b54e5037d1ee4e52308e1973dd8d07e4a51e5b0

Observation d4eabbc1-d1b1-471a-8f12-1d21b5dd0671 · outbound

This paper cites Annals of mathematics , volume=.

Active Learning on Adversarially Corrupted Graphs Annals of mathematics , volume=

Reference 23

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:e5a01ca9868b21b2c37abb5c26c7dd3f9667cbb491050ac7c2a0d08696bf8ed9

Observation 6a7a9de1-2569-442a-bdbe-cc5c7007ab88 · outbound

This paper cites Mathematische Zeitschrift , volume=.

Active Learning on Adversarially Corrupted Graphs Mathematische Zeitschrift , volume=

Reference 24

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:357a0e388dda8770ae6731f190713b4a0661f173215f64d111d1003891d6ac65

Observation 22cc7fa6-dec2-4601-9a22-231441245a0c · outbound

This paper cites The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=.

Active Learning on Adversarially Corrupted Graphs The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=

Reference 25

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:f41b8241bd318a5f6fa39d320e458f49498ede7a9d09f6f1cb7fbe816b31edfe

Observation 7e1f72f7-1fde-4c19-9cb6-8d24fdee3b8c · outbound

This paper cites Forty-first International Conference on Machine Learning,.

Active Learning on Adversarially Corrupted Graphs Forty-first International Conference on Machine Learning,

Reference 26

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:a2c2f6a65d97d9c77e6236d9643c821bf611bb68f991355b0f68e262d4fb235b

Observation fe4e3b42-8882-4766-8f82-37b7615216f8 · outbound

This paper cites Internet Mathematics , volume=.

Active Learning on Adversarially Corrupted Graphs Internet Mathematics , volume=

Reference 27

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:a9711f35bf40474b71ab43eb2c01bfa4d5fb45d46cc60bfea47399c61213bfe4

Observation 39083b90-a499-44f6-9131-f500d7a13f6a · outbound

This paper cites Proceedings of the forty-fourth annual ACM symposium on Theory of computing , pages=.

Active Learning on Adversarially Corrupted Graphs Proceedings of the forty-fourth annual ACM symposium on Theory of computing , pages=

Reference 28

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:e1b6a54d6bb77ab4452e4101d0a7b5601f42e0254ff17967876932110d0b66f7

Observation 4040d0e3-b31f-4564-a316-e076aa0b869c · outbound

This paper cites Solving Unique Games over Globally Hypercontractive Graphs.

Active Learning on Adversarially Corrupted Graphs Solving Unique Games over Globally Hypercontractive Graphs

Reference 29

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:a09831cfc30ef8f5343505ebfe63ec4945c4259275c9a0baf9271144e7b29b41

Observation 62aaf973-e9fe-4684-8a13-27e80555f06f · outbound

This paper cites Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing , pages=.

Active Learning on Adversarially Corrupted Graphs Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing , pages=

Reference 30

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:4083ce403cd5c96a561bcbff282c40ecdb6923d27cf5af8557302543f2ea5535

Observation ed75c37a-e140-470d-8236-6cf054244547 · outbound

This paper cites Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) , pages=.

Active Learning on Adversarially Corrupted Graphs Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) , pages=

Reference 31

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:950caf9276c926693281251033f4a2da1430eb80658211e42f4e2c427fe11b4e

Observation 6f462c29-567f-4032-84da-a557d5e74f19 · outbound

This paper cites Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing , pages=.

Active Learning on Adversarially Corrupted Graphs Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing , pages=

Reference 32

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:d69f65b983bfe480dec438b40dafba4cd787e11a740bf5fcce87f59ef7bf0830

Observation 51782fff-f0c5-47eb-9224-32623fdba41f · outbound

This paper cites 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS) , pages=.

Active Learning on Adversarially Corrupted Graphs 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS) , pages=

Reference 33

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:abfa68a9bc07087bb178ceb55c5fb4b66e87ef74da4788c7eeeda94e45002602

Observation 5f004a78-424e-4ebe-94d8-cb82d4c81e31 · outbound

This paper cites Proceedings of 37th conference on foundations of computer science , pages=.

Active Learning on Adversarially Corrupted Graphs Proceedings of 37th conference on foundations of computer science , pages=

Reference 34

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:1b96a97d0fe84a50a386253d5807ddd495a579bdbe5f1be2adfab9a7540c3306

Observation e954b366-231d-4de4-b9c8-4f2e8e4ffbc8 · outbound

This paper cites 2009 , institution=.

Active Learning on Adversarially Corrupted Graphs 2009 , institution=

Reference 35

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:e2d4ed2f80e242cd01ff49461e12132dc87b0a13ad23bad0b2e7eab9e44b0636

Observation fb6becf9-c358-4622-adde-327bd95bc384 · outbound

This paper cites , author=.

Active Learning on Adversarially Corrupted Graphs , author=

Reference 36

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:dbc05c2a6ece1bbef3a777db8ff6bfeec0be84ee5baa3ee7b2473356789737f1

Observation 70c7d9fc-8479-4a3a-91ea-69136bfe7e78 · outbound

This paper cites Proceedings of the fortieth annual ACM symposium on Theory of computing , pages=.

Active Learning on Adversarially Corrupted Graphs Proceedings of the fortieth annual ACM symposium on Theory of computing , pages=

Reference 37

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:f4a91c8ce4fc8d5d3bda6e7a21b09eea94c76d780b1f266064f8a51a1635b0b9

Observation f41f3dc1-f804-4722-a11f-1eb459d48b3a · outbound

This paper cites Eigenvalue multiplicity and volume growth.

Active Learning on Adversarially Corrupted Graphs Eigenvalue multiplicity and volume growth

Reference 38

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:7e7264c59e7b193e775c9e50bdccb40dd45445892980b842eb7ae4908f4d0561

Observation 8be8c60c-171d-4d68-b7bb-b3a5244e28e4 · outbound

This paper cites Czechoslovak mathematical journal , volume=.

Active Learning on Adversarially Corrupted Graphs Czechoslovak mathematical journal , volume=

Reference 39

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:51af17984afda0a8b77351b21d5e6ac39e85b187a571ef493c15711753ca88cc

Observation 372ebf5a-af3d-4bdc-b2cf-0d480aa565b6 · outbound

This paper cites Journal of the American Mathematical Society , volume=.

Active Learning on Adversarially Corrupted Graphs Journal of the American Mathematical Society , volume=

Reference 40

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:69784c6eec8cf965f78a0c2575b13d6ec5f6d00e7df816063419a8d1d1afb88f

Observation aa0f65e7-a226-4cee-8f5d-82ccea08128f · outbound

This paper cites Geometric & Functional Analysis GAFA , volume=.

Active Learning on Adversarially Corrupted Graphs Geometric & Functional Analysis GAFA , volume=

Reference 41

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:1b3dfdaa544e3dba8f66a7b5c7c08a86eacd4054f23cdd1d5cbd2f3bd4a635e4

Observation 8aa91e07-4e71-471d-b4ab-8ef2aec425c1 · outbound

This paper cites Geometry and Combinatorics , pages=.

Active Learning on Adversarially Corrupted Graphs Geometry and Combinatorics , pages=

Reference 42

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Source-reported events for the cited work

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:5264d2337538527a1ac45b5ae4879dc70bce33706edd58f82edd189666fb631c

Observation 864b8bd5-0899-4ab7-8094-b66de3fd11f2 · outbound

This paper cites Combinatorica , volume=.

Active Learning on Adversarially Corrupted Graphs Combinatorica , volume=

Reference 43

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Source-reported events for the cited work

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:228fc2afbb1b24716034bdceafe701c78dabeed7dd39ecb07442f62c195d98ba

Observation 705b8f86-1fc2-421c-9624-33f88f8a023d · outbound

This paper cites Journal of Functional Analysis , volume=.

Active Learning on Adversarially Corrupted Graphs Journal of Functional Analysis , volume=

Reference 44

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Source-reported events for the cited work

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:af0d5d09604372838866abc4d2c2ece8a1c84a0fb8be356d3610f148640c95f8

Observation 5acd6842-db16-4c9c-b50c-86d0f8a60901 · outbound

This paper cites Geometric and Functional Analysis , volume=.

Active Learning on Adversarially Corrupted Graphs Geometric and Functional Analysis , volume=

Reference 45

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Source-reported events for the cited work

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:78e2d20994dc5028b8e723d5d424814d489fff3400bb691ef697b47205140b69

Observation bf6c4cdd-8896-4f37-9285-750ee60473d3 · outbound

This paper cites Annales scientifiques de l'.

Active Learning on Adversarially Corrupted Graphs Annales scientifiques de l'

Reference 46

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Source-reported events for the cited work

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:ed35e0804fc87459743ef368bd11837caf569e33fc027684201b78b6736c9a8a

Observation 66ce8193-4b2d-437a-a409-600c12d8a1c0 · outbound

This paper cites Near-Optimal Cayley Expanders for Abelian Groups , booktitle =.

Active Learning on Adversarially Corrupted Graphs Near-Optimal Cayley Expanders for Abelian Groups , booktitle =

Reference 47

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verified exact
doi, observed 2026-07-11T12:17:59.538966Z

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.

source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:3a425542cc2b7c85660ef68ed1eccd614c67b634caa70e89a2c0216668ee67cc

Observation 26bb45ed-1b83-4040-b90d-d71604c2f182 · outbound

This paper cites Journal of Combinatorial Theory, Series B , volume=.

Active Learning on Adversarially Corrupted Graphs Journal of Combinatorial Theory, Series B , volume=

Reference 48

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:a86f8d9a2273e537fa5c6b04acc26a9ad3dc6ba162482c1debf0fa1126baa9ad

Observation 3c9384f7-2836-4ceb-9ee8-293362dcfd0c · outbound

This paper cites Applied Algebra, Algebraic Algorithms and Error-Correcting Codes: 8th International Conference, AAECC-8 Tokyo, Japan, August 20--24, 1990 Proceedings 8 , pages=.

Active Learning on Adversarially Corrupted Graphs Applied Algebra, Algebraic Algorithms and Error-Correcting Codes: 8th International Conference, AAECC-8 Tokyo, Japan, August 20--24, 1990 Proceedings 8 , pages=

Reference 49

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Source-reported events for the cited work

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:3c751bb7edde1394720a820057eff2791816f63b89d3a9feed6ced19b059fdef

Observation e037201f-2729-4a0f-9e88-33e5cfca9a54 · outbound

This paper cites Eigenvalues of Cayley graphs.

Active Learning on Adversarially Corrupted Graphs Eigenvalues of Cayley graphs

Reference 50

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:c7d352e0e427daf170344c9df6a68ea1a16219b92c77557a12e096e92d7b72c1

Observation 94e90670-3df2-452a-b71c-474061a268c3 · outbound

This paper cites Eurocomb 2003 , volume=.

Active Learning on Adversarially Corrupted Graphs Eurocomb 2003 , volume=

Reference 51

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Source-reported events for the cited work

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:f4b67eb1ea5d84b1e4d19e952d834514bfa7322d846ce70823a9682b5abe8111

Observation 9e0118cc-fd15-480b-b8cf-d00a4e350135 · outbound

This paper cites Journal of Combinatorial Theory, Series B , volume=.

Active Learning on Adversarially Corrupted Graphs Journal of Combinatorial Theory, Series B , volume=

Reference 52

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Source-reported events for the cited work

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:f7504d2aedf2975b33f0d10e57debee71d7dc54f28bf6e1ddaddf50965e0eb82

Observation 96c907c4-95bf-48fc-b1c2-4ee4080e9da0 · outbound

This paper cites Proceedings of the London Mathematical Society , volume=.

Active Learning on Adversarially Corrupted Graphs Proceedings of the London Mathematical Society , volume=

Reference 53

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Source-reported events for the cited work

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:b1c23ad172424c2a5395b539d6a951fffad918d9baba1a173106f38bf956d871

Observation 08d2561b-2666-4838-990a-927e750577fa · outbound

This paper cites Combinatorica , volume=.

Active Learning on Adversarially Corrupted Graphs Combinatorica , volume=

Reference 54

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Source-reported events for the cited work

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:1aa3744f55e15fbe2e4809fa80b12912f3effdaef138552b118f8c16a5d5288f

Observation 15ac7ad3-2e7f-4d7e-9fb2-19a8424fd909 · outbound

This paper cites International Workshop on Approximation Algorithms for Combinatorial Optimization , pages=.

Active Learning on Adversarially Corrupted Graphs International Workshop on Approximation Algorithms for Combinatorial Optimization , pages=

Reference 55

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Source-reported events for the cited work

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:bd4c40d4e6a73f7e47fcdb15ee448b252b4947a6a24f92bfde092301121fa314

Observation f288f425-ff43-42ab-9c30-ada1f3c8508d · outbound

This paper cites Proceedings of the twenty-fourth annual ACM-SIAM symposium on Discrete algorithms , pages=.

Active Learning on Adversarially Corrupted Graphs Proceedings of the twenty-fourth annual ACM-SIAM symposium on Discrete algorithms , pages=

Reference 56

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Source-reported events for the cited work

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:7a466e20f693277749ddbdc478174c9bc1c023855793b46ffed672a43614888b

Observation e7ade0f1-cf27-43a8-84fc-6692c16c4478 · outbound

This paper cites 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science , pages=.

Active Learning on Adversarially Corrupted Graphs 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science , pages=

Reference 57

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:ffff6e7ffbd77b417c957316950f56c6d1e28f6e1abded2901fc92aeb771bd0b

Observation 2e31d054-003b-4be3-a53b-9ec680b16aae · outbound

This paper cites 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science , pages=.

Active Learning on Adversarially Corrupted Graphs 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science , pages=

Reference 58

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Source-reported events for the cited work

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:558019e8c66958c51682e6a41364b7ecb1a62a704311f9bb4f2eb8ecbdd95358

Observation 958046e8-a957-4ebb-9c15-028982e97491 · outbound

This paper cites computational complexity , volume=.

Active Learning on Adversarially Corrupted Graphs computational complexity , volume=

Reference 59

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no resolver link, observed 2026-07-11T12:12:44.455418Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:6d5de8b453908a410f83525e63a601778ae0b204a9c515d2885f1e884dd1f13d

Observation 76f17284-db1c-4631-b1e3-4d85f44bb11b · outbound

This paper cites Mildly Exponential Time Approximation Algorithms for Vertex Cover, Uniform Sparsest Cut and Related Problems.

Active Learning on Adversarially Corrupted Graphs Mildly Exponential Time Approximation Algorithms for Vertex Cover, Uniform Sparsest Cut and Related Problems

Reference 60

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Source-reported events for the cited work

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:241728a3212b93b7048f33957133dfd0016dd6efd415d7229b24dc7a5856aa18

Observation 720d67ca-c559-48ca-bd76-d4069840416f · outbound

This paper cites computational complexity , volume=.

Active Learning on Adversarially Corrupted Graphs computational complexity , volume=

Reference 61

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no resolver link, observed 2026-07-11T12:12:44.455418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:b32ab097954379878e7fb19dada48aa19528ed5d3a92f2572895810e1279cd65

Observation da55769c-abcd-4055-a05d-1a200993d8d0 · outbound

This paper cites Proceedings of the forty-second ACM symposium on Theory of computing , pages=.

Active Learning on Adversarially Corrupted Graphs Proceedings of the forty-second ACM symposium on Theory of computing , pages=

Reference 62

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:3c3cbb93e48e3c12cd0681573ad378f3c1b10d6098a8f96aa9961f92a3e8ff07

Observation b3c426ba-b714-4388-93bc-6131524e5c86 · outbound

This paper cites Proceedings of the thiry-fourth annual ACM symposium on Theory of computing , pages=.

Active Learning on Adversarially Corrupted Graphs Proceedings of the thiry-fourth annual ACM symposium on Theory of computing , pages=

Reference 63

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Source-reported events for the cited work

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:8341a470a0be9d2397f860fb01a37b289bd78b6d845f5eacb376b7ec202c7919

Observation 2c67044d-ed38-4419-b3c5-dc0bebcb231c · outbound

This paper cites Journal of the ACM (JACM) , volume=.

Active Learning on Adversarially Corrupted Graphs Journal of the ACM (JACM) , volume=

Reference 64

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Source-reported events for the cited work

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:9f52fcf37752bb0ecffc28ca8993ac17ba621454e202632e724dfa96a55dc2be

Observation 30ef8aee-18de-432f-8a10-fad200027412 · outbound

This paper cites Problems in analysis , volume=.

Active Learning on Adversarially Corrupted Graphs Problems in analysis , volume=

Reference 65

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:7dd8c5adac778fbaadca3f0ae2c3c1884aa2c2761f51b7462eae328c1de611a6

Observation 324c7daa-77a3-4d1d-9e6e-906edae041ec · outbound

This paper cites an unresolved cited work.

Active Learning on Adversarially Corrupted Graphs Unresolved cited work

Reference 66

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Source-reported events for the cited work

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:2e11891912efc1c091797627e31ba4cb61cc6cd487d617805473da5870c56e08

Observation 117fee1a-f12a-4441-ad3f-6e294eb02d7c · outbound

This paper cites Proceedings of the thirty-eighth annual ACM symposium on Theory of computing , pages=.

Active Learning on Adversarially Corrupted Graphs Proceedings of the thirty-eighth annual ACM symposium on Theory of computing , pages=

Reference 67

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:b312627b84d50cad73cd03a5b26c9d7ee40a9cf10626bbcfc9c2820cb07fdced

Observation 70d094c7-a1d6-4e3a-b577-b9583a1ecf40 · outbound

This paper cites 2013 IEEE 54th Annual Symposium on Foundations of Computer Science , pages=.

Active Learning on Adversarially Corrupted Graphs 2013 IEEE 54th Annual Symposium on Foundations of Computer Science , pages=

Reference 68

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:dccf389a627b84401ee7117b82f0b49468d0a63a55a84666703838e78928b8de

Observation 5e297f03-67df-403e-ab2d-52de79420fb6 · outbound

This paper cites Proceedings of the 56th Annual ACM Symposium on Theory of Computing , pages=.

Active Learning on Adversarially Corrupted Graphs Proceedings of the 56th Annual ACM Symposium on Theory of Computing , pages=

Reference 69

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:592f3fac4c0152de324973d0288f18ce20c66d5cfb41344da4d5fb7e550c71f8

Observation 3aeccbfa-3027-4de4-9455-ee21435bd840 · outbound

This paper cites Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms , pages=.

Active Learning on Adversarially Corrupted Graphs Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms , pages=

Reference 70

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no resolver link, observed 2026-07-11T12:12:44.455418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:c3f14e66904617ad5d0918e41af4b5483d1c36f1aa5a3bc980276756225fd9bc

Observation 740ccad4-74ca-4053-b042-4e5b71fdcfda · outbound

This paper cites an unresolved cited work.

Active Learning on Adversarially Corrupted Graphs Unresolved cited work

Reference 71

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no resolver link, observed 2026-07-11T12:12:44.455418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:3947f35d50600998a0dac4f67f8e2636636d12b29a11b6e16275bd69130ec7a4

Observation eafad846-90d8-40e8-b970-75fb8d02f419 · outbound

This paper cites an unresolved cited work.

Active Learning on Adversarially Corrupted Graphs Unresolved cited work

Reference 72

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parse uncertain
no resolver link, observed 2026-07-11T12:12:44.455418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:7cf6600a767dcd779cf22fe3710384ccc5fca60addd777bedc69440d98ce8ea1

Observation 2f196225-e88a-4aef-a4a5-5a080321b7a9 · outbound

This paper cites Random Structures & Algorithms , volume=.

Active Learning on Adversarially Corrupted Graphs Random Structures & Algorithms , volume=

Reference 73

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:e14c9ae2115677cbff42c3f80f9f01b914b8ffd7574da46f4819e69e79ecd216

Observation 817bb964-d669-47b5-a4d8-7dda7215d7b8 · outbound

This paper cites Journal of the ACM (JACM) , volume=.

Active Learning on Adversarially Corrupted Graphs Journal of the ACM (JACM) , volume=

Reference 74

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no resolver link, observed 2026-07-11T12:12:44.455418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:cb0c72aecb3e7722a5d92a73e4dde3abd33820436e2f79563409bafebd94fa9e

Observation 0de66120-274a-424a-b16c-7ea974438526 · outbound

This paper cites Non-negative Ollivier curvature on graphs, reverse Poincar.

Active Learning on Adversarially Corrupted Graphs Non-negative Ollivier curvature on graphs, reverse Poincar

Reference 75

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:15f39e76d3d3f5b3b7850341246fced169ddd7e61a9111eee1c8c83468cb351f

Observation 067f597d-9af8-4885-a6eb-c631cd3bd171 · outbound

This paper cites URl: https://people.

Active Learning on Adversarially Corrupted Graphs URl: https://people

Reference 76

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no resolver link, observed 2026-07-11T12:12:44.455418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:ff3e3ec779b6f938ef986972007b0b9ba97a5229f51d93afb1066299881bed43

Observation dcc6b200-811f-440e-a101-7ddc04339321 · outbound

This paper cites University of California, Berkeley, https://lucatrevisan.github.io/books/expanders-2016.pdf , year=.

Active Learning on Adversarially Corrupted Graphs University of California, Berkeley, https://lucatrevisan.github.io/books/expanders-2016.pdf , year=

Reference 77

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:d36b6d2f7276ac28bcd542909b382b500545460631bdb3bc186caa19cd470d7a

Observation d8a42e15-13f1-46e4-a69f-105fb9ccbdcc · outbound

This paper cites an unresolved cited work.

Active Learning on Adversarially Corrupted Graphs Unresolved cited work

Reference 78

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unresolved
no resolver link, observed 2026-07-11T12:12:44.455418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:c3ea226d13d1efa24312c917676c014c35420e63a732edfbe7ec4105e9cc10e8

Observation 6f0df387-46bc-4647-9e92-96d92463f987 · outbound

This paper cites Canadian Journal of Mathematics , volume=.

Active Learning on Adversarially Corrupted Graphs Canadian Journal of Mathematics , volume=

Reference 79

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Source-reported events for the cited work

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:274086229f10380eb3ad8884cb33e02d592211e0864315f83c8ebae1f818484c

Observation 2a5474aa-60f6-4321-8001-61ad6ebc4203 · outbound

This paper cites Combinatorica , volume=.

Active Learning on Adversarially Corrupted Graphs Combinatorica , volume=

Reference 80

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:bd8754c0e664aaf440eb2cb6817ecf05901e060ad360c66f011b9e48e7aec305

Observation b7c5bc1c-c9e3-413f-94fa-4faebe992cb5 · outbound

This paper cites The independence number of a subset of an abelian group.

Active Learning on Adversarially Corrupted Graphs The independence number of a subset of an abelian group

Reference 81

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:c707f430e787a0cc5b6463bc898e466acbe7717cd209a55cd49cfe91c17e7062

Observation 293886a7-d64a-4377-98cd-877b0ed9efbb · outbound

This paper cites International Conference on Machine Learning , pages=.

Active Learning on Adversarially Corrupted Graphs International Conference on Machine Learning , pages=

Reference 82

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:320f56c7d7ae632fbb8507f8dd7a89f67ac04ca362cb2f145d7b6cfcddd141bd

Observation 4b9c2eba-0e55-4fef-975c-b9134007138f · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Active Learning on Adversarially Corrupted Graphs Advances in Neural Information Processing Systems , volume=

Reference 83

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:016ab3cf2daf2b68d2faa97ba9150c5c06c725aa340a784578878290480507b1

Observation d65a42b8-3a59-42e4-97a7-941f4335dc20 · outbound

This paper cites Proceedings of the 55th Annual ACM Symposium on Theory of Computing , pages=.

Active Learning on Adversarially Corrupted Graphs Proceedings of the 55th Annual ACM Symposium on Theory of Computing , pages=

Reference 84

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:78de3b061b0982afecb864429493ceed3cdab6c8e63b6737d4831702fc92ea79

Observation d95066fa-6487-47a8-bd8f-e92a6ec73ff6 · outbound

This paper cites On Rings of Operators.

Active Learning on Adversarially Corrupted Graphs On Rings of Operators

Reference 85

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:84c9fdd76d51c7fe1996a3786124b52b9590ed2c4dc58721947f04aef6b3aec5

Observation 3ed2ccbf-23e6-465f-9d18-daaf8a27caf2 · outbound

This paper cites 2019 IEEE Symposium on Security and Privacy (SP) , pages=.

Active Learning on Adversarially Corrupted Graphs 2019 IEEE Symposium on Security and Privacy (SP) , pages=

Reference 86

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:2f836e86a80c89e984129e6aaed19fe4f1841c9f266aff42d1a4507809741f9a

Observation 3d18248e-70aa-40df-a79e-61fd338003ea · outbound

This paper cites Conference on Learning Theory , pages=.

Active Learning on Adversarially Corrupted Graphs Conference on Learning Theory , pages=

Reference 87

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:a487efe635432b2f767cdddbab603c519bbaff4c04432826d6b2529d440f28b5

Observation 3db50c84-da6e-41ae-8774-92ac4fe0d076 · outbound

This paper cites Proceedings of the forty-fifth annual ACM symposium on Theory of computing , pages=.

Active Learning on Adversarially Corrupted Graphs Proceedings of the forty-fifth annual ACM symposium on Theory of computing , pages=

Reference 88

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:b24108d3179ba407e80ede492fc7c5aaf10e151fe709a06b915291da8181bb57

Observation 2566f6a6-66a5-4b81-a858-73f38a88165a · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Active Learning on Adversarially Corrupted Graphs Advances in Neural Information Processing Systems , volume=

Reference 89

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:6d0656c565adb4a29e2824be1e490396ea54ba88ed81864968b64203f2530034

Observation ef8caf83-0c87-48ab-9500-bbe3380ca6d5 · outbound

This paper cites Innovations in Computer Science (poster) , year=.

Active Learning on Adversarially Corrupted Graphs Innovations in Computer Science (poster) , year=

Reference 90

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:a4bd512703824f3a39e8e31f6cea031f1162c6317cd40f9197375d073750e2d8

Observation 84438339-9cce-4951-b1d0-ea3f963e65b1 · outbound

This paper cites Proceedings of the 34th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems , pages=.

Active Learning on Adversarially Corrupted Graphs Proceedings of the 34th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems , pages=

Reference 91

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:9e70f09a4f1cfe9e381f02d8bba1c9208d225099c4ff997438e40b8882e5c1d5

Observation 85deac74-06dd-456d-8d48-81ba4d1aa532 · outbound

This paper cites rem , volume=.

Active Learning on Adversarially Corrupted Graphs rem , volume=

Reference 92

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:2eb66fe756d6576e5e3ce6cd8082c80145d17896e8beaba3030aabb3ca04758a

Observation 84a8c8bb-75c7-42f9-a9e5-c40d3b5190f0 · outbound

This paper cites International Conference on Machine Learning , pages=.

Active Learning on Adversarially Corrupted Graphs International Conference on Machine Learning , pages=

Reference 93

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:6ec9cb803c06e129df7b7116afc9abf2cc18ec9786bb587ae9e802ba44e828e6

Observation a9af2ce0-1090-498f-bb49-73dc9a722a97 · outbound

This paper cites 2013 IEEE 54th Annual Symposium on Foundations of Computer Science , pages=.

Active Learning on Adversarially Corrupted Graphs 2013 IEEE 54th Annual Symposium on Foundations of Computer Science , pages=

Reference 94

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:2af0d26662ed6abd291ca52653f71d4189b070bce958168ca21b21c759f876b9

Observation ececd51e-308e-4933-9f50-a650c386ce19 · outbound

This paper cites Conference on Learning Theory , pages=.

Active Learning on Adversarially Corrupted Graphs Conference on Learning Theory , pages=

Reference 95

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:6748536206a24c04f6607ff6e3e6c9594b21a579d0f154cf3fc38846a3308f47

Observation 2284a380-e3f7-4cde-8c21-1b4fa9122a6d · outbound

This paper cites Conference on Learning Theory , pages=.

Active Learning on Adversarially Corrupted Graphs Conference on Learning Theory , pages=

Reference 96

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:4cb275226aaac7b2d52ff119bb0f272bdb37ff1a319dc900393fc9bcd19a0144

Observation 3530462a-5608-4fed-87e1-be230a2c57a0 · outbound

This paper cites , author=.

Active Learning on Adversarially Corrupted Graphs , author=

Reference 97

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:2df6dfe2e9c20f3794717cb80bf3ec7fa3967b6f89e0db9f35059e9adaa95211

Observation 021fe835-f962-4b6b-a2fb-f7530b00dd62 · outbound

This paper cites Advances in neural information processing systems , volume=.

Active Learning on Adversarially Corrupted Graphs Advances in neural information processing systems , volume=

Reference 98

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:d9cb5a1c458ed62d29174a47f1c0f3dd9b82f6560b388ed6ecd8b5ea4c2120fd

Observation 3894ece6-c166-455c-b568-b429e8cd3b02 · outbound

This paper cites Israel Journal of Mathematics , volume=.

Active Learning on Adversarially Corrupted Graphs Israel Journal of Mathematics , volume=

Reference 99

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:22e1f5ab51f2ff78d0141f38e73cb18224cc6b0d52b6c3f11d10fc528bada6d5

Observation 835aab0c-6d84-4f6d-9d55-8b2376403223 · outbound

This paper cites Conference on Learning Theory , pages=.

Active Learning on Adversarially Corrupted Graphs Conference on Learning Theory , pages=

Reference 100

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:c26929fbd88ea311042330538faa0eaf3391102429d9866651bad06e0b817e4f

Pith citing papers

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