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Paper Citation Record · LEDGER

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention

As of 15 August 2026, this Paper Citation Record lists 100 of 118 outbound references and 0 inbound Pith citation observations for arXiv:2607.10131.

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

pith.paper-citation-record.v1
2607.10131 v1

Coverage vector

measured 100 of 118 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T14:04:13.095632Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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 118 outbound references displayed

  • verified exact4
  • verified fuzzy0
  • unresolved96
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ffb2a41e-f0e6-4a14-9bca-c135b7ddd4ee · outbound

This paper cites The Accounting Review , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention The Accounting Review , volume=

Reference 1

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:9e0e9658889aaa6cee858165a88a738609d34c9d71da22df1ef52f0fb0476857

Observation 2376e51c-60c3-435c-bc08-7ab46e9089e8 · outbound

This paper cites an unresolved cited work.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Unresolved cited work

Reference 2

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:f12333a6068d7cc49764ff3c642afec26e1275dfad34b37e6d5cac299b51f813

Observation 341abccb-0fea-459e-82bc-d60996e7a0d2 · outbound

This paper cites IEEE Transactions on Knowledge and Data Engineering , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention IEEE Transactions on Knowledge and Data Engineering , volume=

Reference 3

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:c43ec39eb05bf06213b205c4ab7550d166d366df42e27f0f66eacef5cb733c2c

Observation 0e4ec496-d895-4d75-8286-ed0100ed39ee · outbound

This paper cites Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining , pages=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining , pages=

Reference 4

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:48c374a4c31d48649c73f5c34f57f6dc5329c9a28f2a66b5e07c7363034fb621

Observation a5b69ec8-fa8b-4ae3-a42b-fd6e7b070deb · outbound

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

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Advances in neural information processing systems , volume=

Reference 5

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:0997ecf6e2baba413c91708e40b61151a6e68c13e41186f3ce43539a1bfb94bb

Observation 0ef3729f-e8cc-4a92-9a7d-62f7d8291397 · outbound

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

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Advances in Neural Information Processing Systems , volume=

Reference 6

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:368cf03a168113253a5ca08695c6ddec41d1fa9cefb5d71788c1bba0003d2bd0

Observation 1d105cb1-e59f-4d74-8402-0409fcd4e35d · outbound

This paper cites The Tenth International Conference on Learning Representations,.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention The Tenth International Conference on Learning Representations,

Reference 7

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:c66f46fac92cddbe511c69aa39dd56326e54fdb21800b5985c1c96d932dd2480

Observation 36f8a8d0-3883-44a4-9964-05679f3b04a8 · outbound

This paper cites ACM Transactions on Knowledge Discovery from Data (TKDD) , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention ACM Transactions on Knowledge Discovery from Data (TKDD) , volume=

Reference 8

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:15c4caa270850d9d192b0d65789aa561cabf24d0867bed2f13aa3bf451b9ebc4

Observation 7603516c-6246-46ad-89c7-af424018dbbd · outbound

This paper cites 2024 11th IEEE Swiss Conference on Data Science (SDS) , pages=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention 2024 11th IEEE Swiss Conference on Data Science (SDS) , pages=

Reference 9

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:f5fdad99c77a026cce22f3351215a1046b5ac885741420e9b57d4420d876bd8c

Observation 223f7bf2-ae99-4a4a-8217-4f0f5c589cd0 · outbound

This paper cites Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V

Reference 10

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:d804ffe729281256a6daa37d0273ab4c954c5e6ea00afeb3a2a2e4ed73102ddc

Observation 4f3cbc80-9d28-4b3f-9f2f-c255c418f938 · outbound

This paper cites Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V

Reference 11

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:8bdd553fb5e2619b805ed79b272e8571ef253d1b54a5b7e33d38bef961214196

Observation 60643e9b-2dd4-4c5d-9891-7817c19bc16c · outbound

This paper cites Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pages=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pages=

Reference 12

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:a310f7f00a4771bf85b10dff96986ea3df8b2c037cfeaa1bdab951c5d99cd605

Observation fc1ea314-9f8e-47b3-bfae-0378eb3f818f · outbound

This paper cites Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V

Reference 13

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:49ec278cf5d94d7ec0697c996f8d4affd0d31c25ae7bf3062b2b086a621e0da2

Observation 522b1280-521f-4b2f-8258-fd96be17c456 · outbound

This paper cites Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pages=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pages=

Reference 14

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:911d2736157d157147f1c91bfebf29f1fc51147b71607cc7f40cd4809624c079

Observation 7258f2c6-d4db-4e95-b91a-a272d0e1413b · outbound

This paper cites Internet of Things—Applications and Future: Proceedings of ITAF 2019 , pages=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Internet of Things—Applications and Future: Proceedings of ITAF 2019 , pages=

Reference 15

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:2377529e454a1ede938f869e63a163109e83d1e603f13d9ce8b5566e04518fdb

Observation 0300970d-5b0e-46ae-8790-37d7ff69ab16 · outbound

This paper cites Knowledge and Information Systems , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Knowledge and Information Systems , volume=

Reference 16

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:73b673703312a8588912c3ccdaaf1138b03c8942b90224d29acdb64dc1ee67cf

Observation 376bcca0-5846-4927-897a-5a174cf3a628 · outbound

This paper cites Bronstein , title =.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Bronstein , title =

Reference 17

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:5de1d20a63e3ce47b6f648504941844bd0d2c40babd76ff701aa335fecf74168

Observation 46f4f0fd-55ca-470a-986d-33fcbe5a903f · outbound

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

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Advances in Neural Information Processing Systems , volume=

Reference 18

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:a0c105eb5ea03e21627081c67ea14779f641f5e0e3a8dacf2082fa0c7b22723c

Observation 29535009-460f-45f2-a34f-2bce62ed9b6f · outbound

This paper cites 9th International Conference on Learning Representations,.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention 9th International Conference on Learning Representations,

Reference 19

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:61a472b9cff42ee817a96363406ea60c412a5aa8590d526b6c2da631fe829275

Observation f9b8e110-035d-4de3-ac40-4e06f8679316 · outbound

This paper cites How Attentive are Graph Attention Networks?.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention How Attentive are Graph Attention Networks?

Reference 20

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:4840d39804b870d1f88a4df2272da8879b39fd51ab174bd8f2e2679e6a836047

Observation 9446d498-a6d7-46dd-b859-c462eec80cd9 · outbound

This paper cites 2011 , note =.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention 2011 , note =

Reference 21

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:2488e2fc935e9e85e1eb42395450301e74a84713890b6dc18621a3bd1c3e5323

Observation 408fbee6-f238-4efa-81ae-0ece191eace1 · outbound

This paper cites an unresolved cited work.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Unresolved cited work

Reference 22

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:a1022b2e77f51ffc527bdbd19105994562b8480cfa5b5075b3ff4c40db71ccfa

Observation c53c991b-6bc2-4466-9fca-37108c6c5ccb · outbound

This paper cites Graph Feature Preprocessor: Real-time Subgraph-based Feature Extraction for Financial Crime Detection , year =.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Graph Feature Preprocessor: Real-time Subgraph-based Feature Extraction for Financial Crime Detection , year =

Reference 23

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:f60bc44c1a7e26058bdd687323bea6d132684e5af68abee60a5d126ac54fc1c3

Observation 465aa280-55a4-4310-9f10-8f51a3218d1f · outbound

This paper cites Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track , year=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track , year=

Reference 24

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:ba30c0a87cbafdd7428e3bf0d3974be4399d9fff6c7c2bd9e7799a1f07f65c64

Observation 543b8716-2721-44ea-889e-940d4d883971 · outbound

This paper cites Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence , articleno =.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence , articleno =

Reference 25

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:a11fd03e97a82139230439db9b5aa3ba099eb1586abc2d480983beecc8cbb8b8

Observation b05fff32-4816-4f22-ae2e-a8f8b3ae4672 · outbound

This paper cites ACM Comput.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention ACM Comput

Reference 26

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Observation 3d130961-5c8a-478f-a78f-dd4a09ba3ad4 · outbound

This paper cites ANFIS based sensor fault detection for continuous stirred tank reactor , journal =.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention ANFIS based sensor fault detection for continuous stirred tank reactor , journal =

Reference 27

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:68f95898e10a8d1f443b46d36b6fc9ad550486582564e9823f2f2b22b038aacf

Observation 457a6f4a-c2ee-425d-bd55-9a682ba54728 · outbound

This paper cites Spam bot detection on Twitter platform using Positional Attention based Dense Convolutional Neural Network , journal =.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Spam bot detection on Twitter platform using Positional Attention based Dense Convolutional Neural Network , journal =

Reference 28

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Observation c21fb057-49e8-4497-92dd-d61bb8c0ab1d · outbound

This paper cites and Yo-Ping Huang , booktitle=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention and Yo-Ping Huang , booktitle=

Reference 29

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:f4b81fbc12b74467a2f97504bf73542a74c1e3240c49d92b5e1c7934751a48bf

Observation e38f2659-5240-4d99-8b0a-9c90f26166ad · outbound

This paper cites Principal Neighbourhood Aggregation for Graph Nets , booktitle =.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Principal Neighbourhood Aggregation for Graph Nets , booktitle =

Reference 30

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:60780fdcc6ddf276c45626fadfbdc72db1b5517f46061598e20e3ad8d987824b

Observation ad4bb80a-963d-4591-901b-8971b95e0797 · outbound

This paper cites 7th International Conference on Learning Representations,.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention 7th International Conference on Learning Representations,

Reference 31

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:22954012b13831e47d5b76672a3a296b72126fa6b8cf2133d65ff51fe40a4962

Observation 4ff4dc59-7759-4f1f-afe1-e0722ab7a9fc · outbound

This paper cites Graph Attention Networks , booktitle =.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Graph Attention Networks , booktitle =

Reference 32

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:584a014a7f2873c7242f1feac062628f816e9257f2b86db3ba400f5d61616cb5

Observation cabc2a61-6a2f-4021-b21a-4d7f240df517 · outbound

This paper cites IEEE transactions on neural networks and learning systems , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention IEEE transactions on neural networks and learning systems , volume=

Reference 33

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:b13c264c4d0f6d666f4dab394efc55d64714afd8f4b2c155f1a5f34024ddb149

Observation b9509691-cbe0-4cb3-ba8d-865c01c04706 · outbound

This paper cites Big Data and Cognitive Computing , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Big Data and Cognitive Computing , volume=

Reference 34

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:6b257082ddc02abe780a6d22b37099e36dc8e591fe635addade18b05a71f071c

Observation 110585dc-d1a2-4736-823b-7523272f266d · outbound

This paper cites Applied Soft Computing , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Applied Soft Computing , volume=

Reference 35

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:131bc4bcc6bdcdb860d4296e27885338715ae20fdf93ad9de9b50ed439ea039c

Observation bb892f18-7951-43f7-bdd6-d75e55d84133 · outbound

This paper cites Mathematical Problems in Engineering , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Mathematical Problems in Engineering , volume=

Reference 36

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:832baaa0d470e47b29bc9dd04e0dc979ab4da97b8a6292503ef46ef1c63ab2a1

Observation cfc81f4d-05c3-42a2-ad45-e6137fd74cf1 · outbound

This paper cites Ecology , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Ecology , volume=

Reference 38

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:af88fbc9acd8c6f25ff37d80448a671ed501fef4d1056e9e1f2d403f74cb95d6

Observation 6f644f89-426b-4d0f-9d80-252e990c3413 · outbound

This paper cites Oikos , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Oikos , volume=

Reference 39

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:28be56ca478880a9a091fa419b35c7ded1dc4b8ff9ce19d55e2dfafb54a597bc

Observation eb20a96c-d2a7-4d84-91a7-954fe43cb02e · outbound

This paper cites The Bell system technical journal , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention The Bell system technical journal , volume=

Reference 40

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:d6a9c6413d87a5a59d5c7eec7133889124cb0d2be1547684068daa5adda27ff6

Observation a75e4697-f1dd-4e90-80ff-8f6ffa5716de · outbound

This paper cites Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification , booktitle =.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification , booktitle =

Reference 41

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:3da8c13fb803e0f975c452d4d24f0dc100d2d683070bd6b9e1b0bf1771976504

Observation 65701fc4-a79d-4b58-8380-ed5a9c014900 · outbound

This paper cites Proceedings of the 5th ACM International Conference on AI in Finance , pages=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Proceedings of the 5th ACM International Conference on AI in Finance , pages=

Reference 43

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:0dbdb9100b2b9e3aa01a2affba0cf865f57883f80ea9fe53a4123c63f52dc9e2

Observation bf873861-f82e-4538-a967-7111b016beff · outbound

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

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Advances in neural information processing systems , volume=

Reference 44

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:66e841825b18f27fcc2726ae21495a372cb2c3a8e646b9018087de6e73583db3

Observation c7cfc300-a4bd-46ad-9560-b13d91ceba82 · outbound

This paper cites Kipf and Max Welling , title =.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Kipf and Max Welling , title =

Reference 45

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:e28dc3067919adcfc4948ae45b105f7e1e91727baa694f4289c65bfc21a27678

Observation 578094a1-f5ea-481a-be2e-85c4b3e51689 · outbound

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

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Advances in Neural Information Processing Systems , volume=

Reference 46

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:b6e06797818918cc0ea1031e3a7eb83ec7e2060e3e614cc4e88070ee3afdd5a2

Observation 64b55be2-c28b-48f4-b415-886109750db2 · outbound

This paper cites Neural Networks , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Neural Networks , volume=

Reference 47

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:2c275696e0b753c5ad74f0fd11e5138d79d537f11171198c42353bcefcdbb9d3

Observation 5b0997e0-9c94-45d4-88a2-3eb5d37b827a · outbound

This paper cites IEEE Access , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention IEEE Access , volume=

Reference 48

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:2a463b69fddebbe613dc3b2b34c97ae09878b20b0d87f998f526e0e5b9fc461f

Observation c9338301-f67e-4005-bf64-ac56dbe1dc90 · outbound

This paper cites Big Data and Cognitive Computing , VOLUME =.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Big Data and Cognitive Computing , VOLUME =

Reference 49

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:33268422ec385f8e8eeab16b6fc2664476e56a597be0c185699f01f9522d8630

Observation bcd8507a-ca41-4371-b489-097ef4570efb · outbound

This paper cites Ensemble of deep sequential models for credit card fraud detection , journal =.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Ensemble of deep sequential models for credit card fraud detection , journal =

Reference 50

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arxiv_id, observed 2026-07-14T14:10:36.723108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:3fdf160ee8afd15f1e7a348652d40e073007eaa6594a100b52e47effac693c7a

Observation d15eafb6-f817-42d2-bfed-3c3d805e055c · outbound

This paper cites Cost Sensitive Credit Card Fraud Detection Using Bayes Minimum Risk , year=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Cost Sensitive Credit Card Fraud Detection Using Bayes Minimum Risk , year=

Reference 51

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:cc34b2281ffe617cda66b50aad276e738e719743a2e7052416c154e60521c797

Observation b43682f6-8970-4393-a251-d5159d8d9d5e · outbound

This paper cites Decision support systems , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Decision support systems , volume=

Reference 52

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:8bd52337fee2d0c9499fc7a82e54e1ed9bc979742efc2f1ac019093384f5541b

Observation 154ed18b-e5a8-4021-943f-542bcd7477d3 · outbound

This paper cites Expert systems with applications , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Expert systems with applications , volume=

Reference 53

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:2a986ebf3ef041867dfb0d02c95b11837ca2179e2c6965d1510f83c65920187b

Observation 161d06fb-887b-4628-b1ee-c159287f000f · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 54

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:6126584e7162aec6d4cdcaa333d5f50247dfc44e35f1283fe23818ed947f3329

Observation dec12eeb-6f77-459c-981d-11a86544d406 · outbound

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

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Advances in Neural Information Processing Systems , volume=

Reference 55

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:10ce81e5618e1d80ebf23fa9ece1d7cd1933899fbd10cf7caf555bc6f6a6efb6

Observation 0b9423a4-1d63-4a8a-acdf-804bcec1b85b · outbound

This paper cites Proceedings of the web conference 2021 , pages=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Proceedings of the web conference 2021 , pages=

Reference 56

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:f56016afc13ed33dc8acaa5ee792775fbd227455dfacbe8f3bf009628878279b

Observation 9eb80e74-a837-4cc7-8822-24a09c48c572 · outbound

This paper cites Proceedings of the AAAI conference on artificial intelligence , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Proceedings of the AAAI conference on artificial intelligence , volume=

Reference 57

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:4983350c03500106865f251b8afe67655f87a11aed3b133c3c20bd3f487b6055

Observation 568b1983-d266-40cc-a868-2d98098a83d0 · outbound

This paper cites Algorithms , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Algorithms , volume=

Reference 58

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:c0847ea65501696ed8adebc59530f33888bc6c0605a47862ea87df4cd2e21cfe

Observation aa071e94-c1c1-413f-ad5d-d831dc280e9a · outbound

This paper cites Computers & Security , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Computers & Security , volume=

Reference 59

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:106fb7ddf4117b2464dce2c81b7f3dfe9e70a628ba654d61b13019371cd328c9

Observation 6578392c-dffa-40f3-a56a-926d5b23c947 · outbound

This paper cites International Journal of Accounting Information Systems , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention International Journal of Accounting Information Systems , volume=

Reference 60

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:6bfecd3c96e7bf0f5edc809775a57a6210af8753d1287044ba88f39c35ad54d7

Observation 9739ef68-d865-458e-95e5-0ca0014c10a7 · outbound

This paper cites Ieee Access , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Ieee Access , volume=

Reference 61

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:a591de941c64b1ac792e68a0d54767ddd7c576899f1cd3b545f1c58fcc611bbe

Observation 18734a78-86af-481f-a072-916b02872da0 · outbound

This paper cites Procedia Computer Science , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Procedia Computer Science , volume=

Reference 62

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:f38abcef92dee5966f9c86502979d052390765000b8452f67bd304a5b56e37e9

Observation 48b676cd-89cb-4fb4-a07a-a4550abed0ae · outbound

This paper cites Fraud detection system: A survey , journal =.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Fraud detection system: A survey , journal =

Reference 63

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doi, observed 2026-07-14T14:10:36.703780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:dc068c188db0d9f45f400eded95490ecd1528a7d462abd406f9931d842d3ce45

Observation f6d25ec6-625f-4c98-9079-9aa6395acc9c · outbound

This paper cites A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance , year=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance , year=

Reference 64

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:904ebd93edca72c763e12e52ae36d5a231c7f3c6a8d9867198f3975bf902f179

Observation d6a3740d-003f-4b1a-ba5f-71f6190b78a9 · outbound

This paper cites Proceedings of the 2020 5th International Conference on Machine Learning Technologies , pages =.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Proceedings of the 2020 5th International Conference on Machine Learning Technologies , pages =

Reference 65

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arxiv_id, observed 2026-07-14T14:10:36.718052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:9d7e636c7a1e476abfbd0829d436cd50c47b2716737e52bf0af613c86e2792ea

Observation 78b50ba9-e8e1-49d0-97e7-4776596e7c16 · outbound

This paper cites Not All Neighbors Are Worth Attending to: Graph Selective Attention Networks for Semi-supervised Learning.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Not All Neighbors Are Worth Attending to: Graph Selective Attention Networks for Semi-supervised Learning

Reference 66

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:6904cc78e8a52d3ade2de136d5b3683bcf334f098ad6b8f08c51e948e5f88f43

Observation ac0651a0-d5b8-49f0-af3e-e5796004cba9 · outbound

This paper cites Sensors , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Sensors , volume=

Reference 67

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:f298d8ef7ad435fcea9acd0844354819c2fe404aec618b89e060ecd0737a7541

Observation 110508a0-366c-40aa-ae5b-492585177ff5 · outbound

This paper cites Applied Sciences , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Applied Sciences , volume=

Reference 68

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:c311ca7cb70d5a73efaac8a35ae7158508d10905f572e568fcb9b22426ea30de

Observation b36dea06-b04a-4514-aecb-03f7c1112e3e · outbound

This paper cites Proceedings of the VLDB Endowment , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Proceedings of the VLDB Endowment , volume=

Reference 69

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:d5b18b0d6acf6c76ccac58bb4c707acefc81f464ac7274c118d90dedcb91086d

Observation 9bd2c586-8be3-44c0-9f5b-87494dc0f665 · outbound

This paper cites 2021 IEEE 17th international conference on intelligent computer communication and processing (ICCP) , pages=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention 2021 IEEE 17th international conference on intelligent computer communication and processing (ICCP) , pages=

Reference 70

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:219cd0763765fd1b839b8e21bccba44acf317b76375a9f5877a3641bf472e9aa

Observation 108b480f-bc84-4b5c-bce8-83569e5a8a2c · outbound

This paper cites Expert systems With applications , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Expert systems With applications , volume=

Reference 71

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:d9c00d5641663dd545c6820488a2aec71ca15049d58f3564cb59cbbcf5f30f3b

Observation 086cca8b-0bc8-42d0-8733-d736ef5d9a8d · outbound

This paper cites Internet of Things , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Internet of Things , volume=

Reference 72

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:bbd4cd0dca03461ff092917186a5baa6a1e6d9f25234c941b5258a1c00ff8e7f

Observation 56da1829-3491-4950-a759-7cedcbddbafa · outbound

This paper cites ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=

Reference 73

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:0335c52a5cabf905cf05dfe4009d060dbed31824cf517d409d7ba6ae42d1d9f0

Observation 72302e4f-7b7b-4df5-a0fd-ab4d2d6d8132 · outbound

This paper cites European semantic web conference , pages=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention European semantic web conference , pages=

Reference 74

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:5c68f0bb62c31f4dab96d4afa18eafe9692cb9379856db322e1cf6f655bc8128

Observation cabc59f7-c492-4b2d-8534-43d2143b983f · outbound

This paper cites Proceedings of the AAAI conference on artificial intelligence , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Proceedings of the AAAI conference on artificial intelligence , volume=

Reference 75

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:4213a5df45635fab4331b79f9d4da9bcaef58a69be2c3daf8cbfd11e9f3a26e5

Observation 839eb6f8-f6be-41bf-8d73-b1d069de4d53 · outbound

This paper cites Finance and Corporate Governance Conference , year=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Finance and Corporate Governance Conference , year=

Reference 76

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:908038fd287175cb03ad451f255c7a33b8798c4db2ac56a822b73eb2f2a8ed0e

Observation 50d8395e-95e1-44d9-9ad7-b3e753e4e945 · outbound

This paper cites Journal of Computational Informatics & Business , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Journal of Computational Informatics & Business , volume=

Reference 77

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:a85345ea34bf294589e5215e56e7568ddc31aed5e65fa84a80b1b7a27910b4ab

Observation 5fc8fde6-7737-498a-859d-3029c771d704 · outbound

This paper cites Discover Artificial Intelligence , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Discover Artificial Intelligence , volume=

Reference 78

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:5cc60dff1ba87ab6d33fd50972da7a19fc8daeddfbe52aa5b0f5dc78a3d8c5b7

Observation 406a2ac9-933c-4c70-bb8f-badac31033e0 · outbound

This paper cites International conference on machine learning , pages=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention International conference on machine learning , pages=

Reference 79

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:dac7d5d24b46c1cbdb5eee55a0db32f91ad51c9924b7babe57420b6af9390aaf

Observation 89381dc3-0c9a-4f01-8cd6-c79964f3a73c · outbound

This paper cites IEEE Transactions on Knowledge and Data Engineering , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention IEEE Transactions on Knowledge and Data Engineering , volume=

Reference 80

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:b4177ea7424071cc964defead6b5c241606da29182cf6883f99a3d0f744ef977

Observation 06ec252e-ce7f-40c2-81c1-efbfa191283a · outbound

This paper cites Wiley Interdisciplinary Reviews: Computational Statistics , volume=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Wiley Interdisciplinary Reviews: Computational Statistics , volume=

Reference 81

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:e7c35a4c676a0f6fd9ab86f7f661a4027b95e92e600906c1f5eb63d2aa94bd74

Observation 91b89f4a-71a1-4170-800e-2e96ef3ca491 · outbound

This paper cites 2025 , publisher=.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention 2025 , publisher=

Reference 82

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:b5acad54ae8fafdc1bd08b8dafc08e84562929eea3f1e42f02d1f4d5f346935e

Observation b9ac7504-de92-49b2-8306-319b0ef1f4a8 · outbound

This paper cites V.; Egressy, B.; Anghel, A.; and Atasu, K.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention V.; Egressy, B.; Anghel, A.; and Atasu, K

Reference 83

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:61361591e6d165385fe979ca29bb95ae0cdb09d6db82cabde2a186720400d04f

Observation 7d67c2c9-0ded-410a-b06e-cb0be544caef · outbound

This paper cites D.; and Vorst, P.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention D.; and Vorst, P

Reference 84

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:235f6afef73534d2a000be9368fd54cc20247aa48b1f3593d484fe840e97ec8d

Observation 66c1449e-629d-4513-813a-1b2371fe88db · outbound

This paper cites Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations

Reference 85

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:317401755ed948e2e89b001a6452c5e07769c53e2178a1ab56c5d0b2f13c946b

Observation b3dd105c-c3dc-4798-bca1-c16651577b53 · outbound

This paper cites an unresolved cited work.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Unresolved cited work

Reference 86

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:2d9f6ff91d978d09b7472fe852992ede615bd1b0fc1ec69acb88b24ee06b7a1d

Observation c27a4f60-c5c7-466a-8ecd-5b315c3a03d5 · outbound

This paper cites an unresolved cited work.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Unresolved cited work

Reference 87

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:f816ce0bdf8a3e742df747e825f49317cb37c4b95d36593e84e7e7a868e0af87

Observation d7ea90ea-904e-40b5-91bf-05e1c3e07fbe · outbound

This paper cites an unresolved cited work.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Unresolved cited work

Reference 88

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:db437b0831f4bfc8b3445bf022cd469f13be03ac7fd64a11cb7e431c3a0c5121

Observation 823b7f63-565f-4277-8c6e-a1085c961556 · outbound

This paper cites D.; Teoh, E.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention D.; Teoh, E

Reference 89

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:35eeed30ec85f5cd8b65a1e31616bea78b8a6c35179395311631becd149ce709

Observation b9d44f6c-0dbc-44a5-b27d-46c044ed94c9 · outbound

This paper cites an unresolved cited work.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Unresolved cited work

Reference 90

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:74df4d4e06939a272de8650b4d21f235a6a19d6a83e11e5635588f7d9ff11c7e

Observation a57cfa6a-260d-46a0-9cfc-e9f7fa88d19f · outbound

This paper cites an unresolved cited work.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Unresolved cited work

Reference 91

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:8b4a4cf0dc2a2f17c46ce216e88547be801f34305b6494730d860064b32adeeb

Observation 238810fc-47df-4618-b418-ab399d0a8b5d · outbound

This paper cites an unresolved cited work.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Unresolved cited work

Reference 92

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:2041cdcb636fa8a1d72fc1bb2a82763267abcf677a99965c54bb1e8d67eb0bc1

Observation 75831e3c-904f-460d-a121-c145293a397e · outbound

This paper cites TeMP-TraG: Edge-based Temporal Message Passing in Transaction Graphs.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention TeMP-TraG: Edge-based Temporal Message Passing in Transaction Graphs

Reference 93

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:a067cab480214eab46871c12dcd76ae8ded2edc8b936b7d9d1ccbf05966832d0

Observation 989313ed-4362-4736-a6d7-54bdffaafb01 · outbound

This paper cites A.; Beutel, A.; Shah, N.; and Faloutsos, C.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention A.; Beutel, A.; Shah, N.; and Faloutsos, C

Reference 94

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:f0dff56b9fe9615ebd376d5c285bc9c06b471bb911bf609bfde6349783bed94e

Observation 2ceba9ac-0e00-494d-85eb-baea9fa8ca97 · outbound

This paper cites T.; and Villar, S.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention T.; and Villar, S

Reference 95

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:956db88ef5142034ebc94335463b19f3ad5ef823f4cdab4869212f1f76f5f5af

Observation 0aae3f5f-065c-4f7e-9528-4ba065dcc26f · outbound

This paper cites R.; Hermsen, F.; Chala, S.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention R.; Hermsen, F.; Chala, S

Reference 96

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:af783c4579fe899cb5e0f16aefa4fd20a67b12a7a7c9848fc179ec562e4ded25

Observation af2df374-dda7-4221-9e90-8c3cfc2a98f4 · outbound

This paper cites N.; and Welling, M.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention N.; and Welling, M

Reference 97

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:23386bccdddcdbd46e3d53c4c0027be4bcca2a55ad0247ee0111a60642148e89

Observation 1a52ca06-4b32-4025-aabf-b178fc2ce64b · outbound

This paper cites an unresolved cited work.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Unresolved cited work

Reference 98

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:403b6a3f682ee760b14de6f60e4bf21da2fcc954a4ac919ed2e4d978d4a6b2fc

Observation 9161f716-6a14-4684-8ab4-180fb6efd9ff · outbound

This paper cites an unresolved cited work.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Unresolved cited work

Reference 99

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:368935999f9825b3c02a8790f988d55e2464fd4428927a67a946922369d0cb8e

Observation 7b10908e-4626-4dab-a0bc-008aa312fab7 · outbound

This paper cites an unresolved cited work.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Unresolved cited work

Reference 100

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:a2536728da9ceda074bd89146fb69ed00d845f04471898d9b3bc9961dd41a5c5

Observation 8e747179-eeb1-4584-98cb-ab5f45acd2a7 · outbound

This paper cites an unresolved cited work.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Unresolved cited work

Reference 101

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:5357db0ddad61cb714727aa8da4c38ac93e5a45155b7a3d0472b6c3ebfb1fb2a

Observation 86b59ad7-90e3-4257-b206-5608de29da4a · outbound

This paper cites an unresolved cited work.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Unresolved cited work

Reference 102

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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:94cf014455143a1170d9fba4131bc00510b2ebf7bb22e394b7ed737481e07f97

Pith citing papers

No inbound Pith citation observations are available.