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Paper Citation Record · LEDGER

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

As of 13 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 2026-08-13T06:32:02.005865+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:ab99d2e2bc29e79c9948aa606bf1cbbc2a8fa88ded2c070bd4025ceca6cb2ba2

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=sha256:22e8dac57ee9149a4f89ac17b1ac352063e32ca4ae9e9d08ee720f5818a3b6c3

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=sha256:2b8ab951d1bb10d719337fc60f513164f86057d1e901a973481f4969896a2449

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:cb76166ad66a7aed1c4bb19a0a126103ad747afca59763757f52b4c0dcac0855

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=sha256:530d63ab33fa08b352b92744e95a7fc7242f19d75dc7f93a9220dc987c8462eb

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=sha256:98baeebd6e711e5ef97a3bbdb162d6928c87ebab04bbf09884fb697fd0bb1134

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=sha256:6e16bbcffb2abdfb29b8cbd27b28d82f2cc3f989abbab62645f41321107bdcea

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=sha256:6b06c4d9c1e7c00c12207d5e6efda962ee17c01ae24fdba29739aa7a68881e83

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=sha256:137cd2fa636d85f2476295657bf73bab13d9a07b57e23d9f7d2817a26bf3b8f0

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=sha256:7f7dfcc15a90ddb8bd04ac4c559d21b5ee5a8ec7f83a554017f44495c07f16d2

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=sha256:326631676d84f7961c56f4f1ebfe97c8cdb6b791b142c7ee01b391b3b9feb7a8

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=sha256:6bda15df43ef03ba1279800f253fcf9750d209a1dfeae81df52560cfe4b76017

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=sha256:81cd238b7e3e9f4becd5e77d6ec829cda99e68c0186e4540e55dc2e857741797

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:ca0ee5c572bcf1d3d549cf91725aa0530386870583a5c4a17cfca1b6c9743b31

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:f1d4ef4fb6aa6fa5d689ddda114d7f1c8bf05d8e456cd053a26a2df998585249

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=sha256:1e26f17a8c0f0ccc001765f6c3b13fb6071a47df7ae2abf19e7a1364219cd994

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=sha256:3385b1c4022cefbae2a14e183ed5e54e72bf2bab39980440fc0178f49c333cd3

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:babe2391ea6a3d5f7df93f45cb0e973c603606544a96d4ceccece7456892c45a

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=sha256:69e206d3a10278560786491d97377a9056d14feb66f0035f671fae57adcf9254

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:f223be665610ddb3515db2528ed7b31fe6dc7c8a52bd1495e5ff481c3f114053

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:f611bdb883111a15a1134c85118124b34ec0ac245d6394a085189a36b031f72f

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=sha256:39c1cb6aa393d4b2c83cb8489fb3cd6ab54023bd20746b1ad899f4f5bd489620

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=sha256:8b706b8addf3d1006a78337121f19a18e49714f8b514bea4faf30da55854ae27

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=sha256:58baab01fdc35475d0bb57d9b2912ba1c9da8c18cc3ca96bba334dbf780a2b8d

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:e0cd829466a4ab2503f317b6bcfa0bbcf153eeedc4a1eccd9b330734b7ef7c1a

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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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:e4f849a6bd54ec4ad6cfc873e4db386c9600d3fd64a9df710133d6f93d629435

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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source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:1c8d17580bb5fe3ae1e61d5ac99915f7096ea8d6456776f662d4c52a7ed96832

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:fe1ebb0a47ed4bf61d2d05c03378e74af729d5221606f5ea0fad1a12b6916fab

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=sha256:54862cd51dbc6aaad9b2b67a0cea03e292a46819f6ee10b15e522adbfcb241ce

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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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=sha256:23bfc42f9a6d344fb65b63f51f32e5dd633e2b7488cd27342adbd61068b4a929

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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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=sha256:4b5630728c1b67973b244ab4753be1dd947c64cafd0ba589f637cbf45b379103

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:ea5dca4f6358675385aac2b28cc5a79cc918d97f5d21c499a28a5c4cd7cc6101

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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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=sha256:64a254e95adb1bdb9f5bc549ba1bc2ef17afc4ff07bcb12bd0b541941d444e1c

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=sha256:8753acaab662dd743467ac3e585ae12667aaff87b0b3c2a34fff95ca40fbdd15

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:ebe3c649871adbb9dab6a7276a2a4a52cf75baba8591218fa4132e8fa1a54ece

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:c4ab247d5fa71fbe2bfd13c8f9688cc87d5d811290372eb107a456254509ac6a

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:cdb7d9133de2b04eeba482cf463678de539404ca6a12106f1382d18c97154544

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=sha256:0d70e219a97647b30037f921bdb7dd98e702dd25202c12238e375cc4f4d5bcd5

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=sha256:1c4a5ccecd39013de443c6a323644cd680a331d1cc573a0059715c70446bf625

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=sha256:0a6f11f2cba1519ea9f8f35d9975fb197e70ca7426a347a6dff4197b553fef27

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=sha256:0c34d1578b48324886faeb44698e287b0898bf64a1e3092aa1be65468601234c

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:ec4aace2b26c3aba46ce9f21b8eb5c79397edafde8fb313943d6d84eaad8795d

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=sha256:43f22c52b8ca9e4e5985f4cf23069cc09144d438f1ed7193b760cb35d0e3ec5b

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 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:86369ebf5eb703efc85819ea968793c27f1ff86c3c800e9d093b208fcdc664da

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=sha256:50541fb68626a1527f6de96a59f75618bdc8f43d8201931a2528f00024086354

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=sha256:8b56b345bee08d63930d158111798c65057d58144cde953f5e4482c59cd16c8d

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=sha256:658e55829d372ebda90f28188f76332f1094292bb582086782f635a575ed79e8

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:ad0c929f26e417a4247d82f3bb28ef7dc4bd092cca3f6009544f3a86b9db8425

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:b489472120f2b6bf5950cbf408a8b7935c8474278f6991891fc6403f1dc80cfb

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:c6f8bfd41e137ed0ec55b2e7dbf356d4363ec7262ecde2c315787f128ca04496

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=sha256:1018c276538b8036e0ee2b43b04fd9d3617071e89002bf2c73e2214d417a0589

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=sha256:50657451a8005989d85b46305921f83a8edd8b757d836c63d2fe77a7cc7a59b9

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=sha256:3513e84852b5c13cb8841224eedf56502394ade0fb8feb084fef222f3015d1bc

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=sha256:1e7eb46cf603736dbe346e0a52dd68bca2be7287b6c50b3843e6076930e703d8

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=sha256:5243ff3fe56b7772fb011c1b48778804ff50b4f88747785e7c3fe85f52398f3b

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:eaaf83ba0897597f472e2bf1f633902f5529a668d6eaee9a734749e879dd6346

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 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:6125567b8bfd4690a181286376cf47af005b2d13fac9e788226de4f1d266c622

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=sha256:01873d0bc1029bc3f0daf55274b8f1c7ecbdba9606339a09ad318f32fa228fa7

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 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:4f3793605a85f81016ca0038e89e1d8392e69a91b912890e3539567fec11529a

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=sha256:6b1635d91fd586df884e37cfb16f0179c7a095ab39bb21eaaee7551d46d45b8b

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=sha256:7d5bf322a74c2e45307abdcc12199ce60af265fb1dc5d9510cfef6bbfcc910ff

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=sha256:2d560e285af69e887b0f39c365230549140d8b6f4347dd93f91cd9f77418c1c9

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:eff378da36a5b64f5d5031bfac55714a4cded25bda41e6321819a0a46c80d2e2

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:ff59a7935c587d46d268ab0697d55e9fdd4efac6a64ab3024ff09dcd637939de

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=sha256:8638a7ba9d3fa6b179c2a0d8ee417770477e596c5b8564102a6700b2ecf87357

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=sha256:512ebe3d8b1063c482019afd36eaa214cf12ecd73be4c9ebfe24cabe682b8a67

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=sha256:0dacb216431f4fa0a3aba217ff50219692da496f16784d16e1897355d718dafc

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:b08f2604f5c8c6e4ae64651c28ab12bc43d809002bc083c733072ef8542d74cb

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=sha256:9f76c6eb7743c9a3a9d9731caeca65b93717d7e4cae1894242391e5b5791d0ae

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:d67ec56e10af0a8fe7286117b6ec62bd49bd037eda31ed26c5a57bb5bb0c1b49

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=sha256:4117af410639a5a91e4d2ce059b2a6ea070ba62c5e7b5d6dcaac8c660896b0b0

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=sha256:39ed27b0ba528db999a980c9baad660b49ee6546e23e3f36525e2eba6005fc8e

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=sha256:65060a7142874cfbc98ea6613399f3d3d26ab9d0f6723763cd22d19cac4db136

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=sha256:2557034a435850aa5f9a0c290414613626077540a57edcf962ef157bd73eb185

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=sha256:990a0cb1dbd4701e487191222cee0cbd5334aa573fad708edefdb009aef4d86a

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=sha256:80b04193f7c20e34784467671da72a6df6b41359bad34df14ff9c8ded2581432

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=sha256:804516a66e76de496ab7313022d497f317b1d4de87f6c5e19454e8179c1dd13e

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=sha256:60b8f6a1806f8933462f5c7197105a6604d59d755fb7ce7f62e4f5d19bf2bff5

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=sha256:6bd0e99718bd8022c05cec8e71d1cf6d1c1bea1d8c8b63ff50a2f50ca530d746

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=sha256:31ae0ee3b56adf756dda96e5e4ca1e9468424a47d1c73a209bcae2b29c162032

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=sha256:4273457e7fed7cf581278479ce6b3f5f895645e7a85ab6d652ab8436e2a8bf32

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=sha256:5a14da787ab316843e5e6aae7b180d93bbde99881f843426d6221cd2cea3410a

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=sha256:4076769363cefd5d614358c5948c1292aefc452bb4752d785819f8765defc555

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=sha256:5dbfdd0996203df5660c27e430c6db1e93da49a85d9390c447c609826e15283f

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=sha256:66fb9dd291f9b0d37b9aa60c7ec0b4ce1d366fc4e2da3c63a96fec802e491343

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=sha256:15c5417e60f39a7e3f1fd3f2d3fb6d32895b4263a4d29768ec9c16e9d014b359

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=sha256:0735ecf72492f929887da6f104d28c20fbc45d2df62f0cc536fae14e3eaae7b9

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:f728c13d28324899866b35fec7aa17e8766fbb50cdd5751f65c2f993b5a08372

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:aa26bf7ccf093204c3033633a1f0035d9af3587026578b94880ee6fdc547bd78

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:f7d1dd6dc04dd374e556c1f211778cf3f4576c5ff8d39472ba83a64670266d49

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=sha256:16ae208d36ecc86ab1b6f7c7ebd46196e4b536d7316303d7315e648d2029d850

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=sha256:7acd26579c17b85fed82c60bb8e07ca9c8d80ba3691dbca2c08435ad8a97d798

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=sha256:6b6f709ba1468e6e748f96da56d3be24b968ab0377ae2bfd596a170bb3a239a3

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=sha256:229e969b15c117133f189137841c6018f71efc275988248eb706a1747c8c78c9

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=sha256:6bf9f796ac77b70c9ca85dad1cae2a9eccd8a1277e21f55fddfda7515d02e911

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=sha256:150ed8cbd80bbe8ca4476ae784070240d1b0e892ec85daba3bb34d4971c3708b

Pith citing papers

No inbound Pith citation observations are available.