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Paper Citation Record · LEDGER

Active Learning on Adversarially Corrupted Graphs

As of 14 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 2026-08-14T06:32:32.682623+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:e940a1b5d885340f9e5a0f56c61d1f21a9f279e10e19427b82c9be6899cbe0ff

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=sha256:174919c5c626351d8792cfaf78c552b34a02ec9be15848028fe478e83afc4724

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:cb1fcc2407e0d1be120890e9a11e1b073b9d104e34efb1fccfac27695535ffc2

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:ee4b10428bb86297be281ed1da85e3ddf0b1635d29939ab8c6ee7c0d34904d72

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=sha256:85d7a36d9e68d9b2e4d9aa901dbf58aa6256b729760ec4a838971fb7627ca565

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:f5f51d282385a9ad10729ab8474df2dc198aeae35054f6c8141c883c439d39d4

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:a435b8f4559870ed2170e2c829651025c2c531bcb05cc5fc1d709eef607d36d3

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:bda0f4bd9bee0a5a4d8a5595c3b70bb5319e6504b419b530711a2a8a016e1da4

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=sha256:358f1e6f7151f7bfdf7702ef96319b1fd955e4e388557adca721cb94266cbfab

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:f4f9eee5303ba3c7f5a8e2407ee097d5ccf51d05dc7763b6a699ecefa53696b9

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=sha256:3c127d2b001a3a8a3ed6f1be36945a045802e7e89930483767a83d2f351c45eb

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=sha256:8cfb60e2702f2e018d1db363f96df6dd91090da60c1d17a0c8f1c6d682b08976

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:ff131f5871a82433256ebddbaccf051541d77e6c88cfe8aaeeefcbe2e9007876

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:e84eda31ae0a0c1faf0a7a3f6ef399fd72971dd7b98f06614f7a52cb4aa1dd5f

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:b24dbeae15c62364aca975d7a9c9238bc6f7d64840f8b7d82afb3f9f314cf989

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:dc41d70b0d811bd7c2caf611e4ce764bef4cbf64084747700dc99dc429f2208a

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=sha256:888e776b37e4bd7ddc03b7645ae3ec674a33a0d0ec954c5d3ebc98b93999a7dc

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=sha256:4a0662a02eca775864eb42f8fa40403f1336494f853a8b0887327ee3bca28723

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:ef9132464161db0c38ec307ac5dd0b5ca8bfb54716b1a99d82cd60fe614940c1

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:fefc365fd738e14ea72e3bc6fbf02f90be67c555da189411c35a6c77c1e1a8f9

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=sha256:163b1080d33b6132ea19b9e4377e3f274919bc8e4cf2d27324c2264ce6d86b68

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=sha256:649b5bd98f0b0207b69a7de18cf9af1d1da689b55e06b8a92327db2e1d435cfe

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=sha256:6b36caea2d5eddb967aba03ec8767e35f47d06ca23f2956e66a7cf8dfe141e01

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:c6490dd443165c231c64bb8410ba34ca6490a14681659cf15b80331f7a0d6f8a

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=sha256:27a439026436ea2557cd7dbf0ef92f1c5b2e850166706e9702d86f5a40bbf497

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=sha256:427b97f8afa2c1f116a3a502101bd3be4601c5f87b5ffbdb1a14ad32ed852de0

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:f5946f082e37e6373f835fb6f09b48e7295aa34ece3a7f621d43b8efcfa047eb

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=sha256:7d7befd306d35f542b281107899e9637aa9ad306d9a9ca6c0bd3141241f4a1f6

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=sha256:2905743549b3b95f8e33eeb484c266621abf58e24c87acfa9cb9215363628638

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:ab73700db55331d3e7c2da79946d130065dcaeec643d86c794444fbc1a388994

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=sha256:61ebce739c2c891611ed960896d4ab007eb15531c482c8fb0c569115904a22d2

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:f8b568fa34cc88647e72858daa0fe82e0953bd3191afc053164d2e42d97bb067

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:c73f49432f89c4cdddc28f066e2f120ebafc3a83691668b1a18a2adc6eb3eede

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:c47a5c34b07b438da6255213c322b204c22cc5f0c1bf04a62644e21a8aee94b1

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=sha256:0761cd0de13e7ec8986a3dd2c20a5c4345f0fff140fb689116088004bbc10ecb

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=sha256:92d0bc0a3c027f3377bbd188e6d7c691b9ba1d34b79d625196d078fcaaca7745

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=sha256:305a8f8c9194c150b24d848367b3568bfb285c8937f0372961e76123db28f5d7

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=sha256:1dd701db977a569ff5681a791f62c4f6b4cb235b3d0e9ae2a3143c14d1b8aaa6

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:ce889845448e491d606d6c9019c598460d73eb04ff249b497d0787788f3f428a

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=sha256:378712a12b9b6baae2a2d8c82e728d3995e055c7d47c336591383e9ce43a58e7

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:d07de62b0cbfffc01c171a16d8aab5027d45c5f17a1976c20fb27ce55ab53d98

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:af2bed5eafb6ce6f5e680b40b61b1210925e85731c24f1e060ae92f8ba3d4248

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:ede60428c7351ee6cb22a329499821c4d9aa90a357ab11cdc6ccb33daa93e462

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:e875bbef516ffc50bfe2f3618d93b09c9ddb57527887ebf971587b3d8f449122

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=sha256:989e655e14a3bc05b5f9b8531414b0bda0ae6c9ec8440aace57a38532f8663c5

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=sha256:6efd8e3b51e9c16f04fb0c09642c459667372760bc2392e415372d6731b306ee

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 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:d24c711634fabcaa3017d34618e49b471bd9d9539e24a5494ff0cde8f55b3f9c

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-reported events for the cited work

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source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:49cde222e4fe506fb9d4442dceb331b750f6d1a1afd50f03c5eea27cd9ec8c97

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:b1749f5c001f5a9eca89302a78a7d8904de709d8f7efa650cb2cd9841b9f2099

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:d97e5828f9d4195f9572136c0a4522d4853fb602f931ddc10f70edc88db4960c

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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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=sha256:35ef4f9ca54fc16de3a96d7be50ff6cdc7a3546d1e5feafb7589099bf96702ae

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

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:01fd1da00697f1eb761cbdf70b2017df7f7204037ed60b96e15aff0242938a09

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=sha256:78ecc3823e727ddacff503a03a2e2de26f3068a92ec4d6326a426873f9b1dab9

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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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=sha256:84ee33398e07124cc49cba3c5197ace8d8bf72e94e03f6b74aab056fa9aac664

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=sha256:34991b404fb881d7bfeec24c1a7b0f2b2b9d90d7c1993c470e36cf3293585461

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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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=sha256:2abfcae518f26a99a6dd0c1bc79fb353629ae04e2a8012081340a3d3984493a5

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-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:91665e7ed1309cd4fcbc1799427ca23d0db7a8a888ff73a2b6522e7c9063c537

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

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:690de30ab5d4689ad051a93df0ae93f4aa9bc71d53d7a813419263497b2a0d77

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

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:138bf563d3bf752656cebd63d0f2b4fd17a6acf0d4b7967b58c04fdfeebc5a04

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

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:ea0508e320502baf378dcc7778e4b7964b4d716222eb1de683c6c381b31ccc51

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:a8855a0f25c23b595e2595c19f6a62eb781f8c6e62f2b0848e43fcbf694485cc

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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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:d71113142a5de40833d95f1708456d5511d8bfadc35ca7627109323c423a5a51

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

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:b0775c60650162ffa59a481fdae106ceda2019f39de37c913f828f2fad6995da

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

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T12:12:44.455418Z digest=sha256:77a538f908233a9d201c6e0e8bfcb1ef670a75e586dafd8943f4f5ba10eae386

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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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=sha256:2464151603dcc6f3a037d75e19af43edc6b2014f035d272d07b130f09975d24a

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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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=sha256:777f5b42f5bee9734f2413a44e901162a4acf9eea149564a04443f9eaa1a579a

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:a356f2422eba5ecc83628661017b0201ea6af3f0e46b40073957693265efe865

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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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:d15f4268651f1eaaa1adb9a675f5f7f771ce7f583a15081461a2b3f84c16ad8f

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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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=sha256:4df1d12e1e061998d591751ce2d18b996160472f1b65948530248873adf1b9b8

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:b11454c642f58e5ec0c9a8bf5451917abb9ad7976437ffa2ba1f369ffb4f2b97

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:a6c7dfc3e422b61d3b322a66d6b8c744ea44f8dd97ee764f080106dd8adabd3d

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=sha256:6256147dec93eaf50ccee40c52e14a90ea1ff43dc8a4c10dfcc983db84e8f12e

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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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=sha256:7da31be52dc02c0c01000b1d69e82ef469b5280153e58ad84897902cc8abce48

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=sha256:357ebd21abf9aa30edc01b96797e0314f24705c52445307b58e5468e5ea89dd4

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:aca6c73dc55539907450833736815bf63eea6d5c70c41146e7fe5a4176298c09

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=sha256:4598d039b8fa61fc35539c962bdb6bf842648f8e8a9b06a4f18756a40da48169

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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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=sha256:15695cc3e337dd4e30ca2e687da3763a0682ca7bb264d162201db82cf33fba64

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:bd575e6726723f2f351191b723d27b662c73acf2fcb039f63508d76a21d0d025

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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unresolved
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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:b56b69fa92bf81c171767284444d4c8b6ca2842a32a06c16d7c88aa181072ee2

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:fe2d9c1157011b859637d2de19267a08753bd994e4a1824f9e20b8852a649504

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=sha256:581b23d6f485c1e9e9296ea5b2c0466231af63e379442feaacf5f5c30b7daf1c

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=sha256:0a463c9618c331511aae88793d6f6afd41b2d709956f7823f392fc8455d43308

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:cc3f17a30df266b31a2e649ccf957b8b6f81f6014bafdf8dcb1d259acab72700

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=sha256:3877e0c67791222a56e1232c8171fb5832fbcc3eaead9564110b400227dbdbe9

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=sha256:80d9c1ed77654cdd972fc9facefd6b9dcc53e9f68dd38a966ccb1cab3d32e414

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=sha256:46e14576b95c6d68ca6769fc50e5bdaca5e80c59b4d55bcc7b4d6f05061fe38f

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=sha256:151e64ec5dd220c8f1dc9bb410ad1961393261c808bab74c0ce2c503b20cd7a0

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=sha256:1b271dada7411bb73d9ee00a9fa1c00451ca42bca6191bf97f2d1734f590e342

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=sha256:13b2847fc8ceca6257c4c20d3e0af94adafa61d58d1087dcd778cafe24a205c3

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=sha256:551cf7037fe84a5aea3b173137495adbcbbdb19da695b9b8ed96d9a0e7c7a33d

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:a1aa3774aeab6656fe4c6f6522922c9faff1c443ae7c985a4e83570c34358971

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=sha256:2621096a68b9f76a97527b3901c44828a02975b66d20e2caf70297f8a98e2149

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:fce6a80de401b80cf473994be4b502622b23206e6e7233dcc9ff8ddc195f2d54

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=sha256:74b0e409cc70fe6858a4d284d2804ed94d00e8d1fce589e6e98e2c5885098abb

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=sha256:41a5457c060878c8964b970d672cb66c95dc8f2aa53fd6f0bc932ab07d07afd5

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=sha256:24d809170e900c071e7cdc6c7de12a08a3309a1e29a0680641fbb4100bac2c48

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:c676e3bdf1529b776ee6ad72c55c2bb575d6c7d11b3b493c2778f02338c7825e

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=sha256:0fad22b3d87ff145de5c338649cf4dc197a2f84e564eb34ff822861ded933812

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=sha256:672429a774f4df85076e24e680768341535e25c128672d360b3354fd8aae22de

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=sha256:7c0ce8c6c3a60a7453c50fc9b547229405a4889dfe8004014a3c6f024c3db26d

Pith citing papers

No inbound Pith citation observations are available.