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Paper Citation Record · LEDGER

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations

As of 15 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2412.00241.

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

pith.paper-citation-record.v1
2412.00241 v2

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:43:50.298140Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

  • verified exact1
  • verified fuzzy17
  • unresolved21
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dcf07aba-a2f4-4ee0-aff1-6c8b6926c5af · outbound

This paper cites The Surprising Power of Graph Neural Networks with Random Node Initialization.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations The Surprising Power of Graph Neural Networks with Random Node Initialization

Reference 1

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unresolved
no resolver link, observed 2026-08-12T05:43:50.118201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.118201Z digest=sha256:2735f2cddddd85d9f5fd472465365b48b67c6aa64aa33162969efd9868d9eca0

Observation 13fa6b79-4b64-462d-89b1-6b38e0e32593 · outbound

This paper cites Realistic synthetic financial transactions for anti-money laundering models.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Realistic synthetic financial transactions for anti-money laundering models

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:51.042904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.123803Z digest=sha256:6d91a5d858c2aaf2bb5d3fd954bbadb72e3f0e448a79c9315ec194f3a2c73626

Observation 7a657842-78fe-41de-aff1-e32b3a1101aa · outbound

This paper cites Graph neural networks with local graph parameters.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Graph neural networks with local graph parameters

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:51.027602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.129285Z digest=sha256:c91880e69a49d2cb7390f4e677ab5dd653253aa75e608676b19467731979a374

Observation 201331df-b844-41ca-9600-b7a736a09f44 · outbound

This paper cites Bronstein, and Haggai Maron.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Bronstein, and Haggai Maron

Reference 4

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unresolved
no resolver link, observed 2026-08-12T05:43:50.134468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.134468Z digest=sha256:86b1fba5d2e8f16fa26c3eb02fe87e0fbfc5ada03562c699efe59760ecb2ac43

Observation d3e2e8f2-40c1-4d58-8156-6ede2dda8b96 · outbound

This paper cites Efficient subgraph GNN s by learning effective selection policies.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Efficient subgraph GNN s by learning effective selection policies

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:51.003386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.138995Z digest=sha256:e37390e8bb948ea2b7fff580a0abdd65b7f7a17377f91dbd2c083522c4f35795

Observation dc8c5a24-bb4d-4c43-9497-1003945e48ae · outbound

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

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Graph Feature Preprocessor: Real-time Subgraph-based Feature Extraction for Financial Crime Detection

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-12T05:43:50.144064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.144064Z digest=sha256:bee7521c3b2287d4041e6c588f85b7047b428d63fbb8310ce05874e6c9c19d02

Observation 34831829-3697-4819-87d2-0a1a72c1d2e6 · outbound

This paper cites Bronstein.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Bronstein

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-12T05:43:50.149982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.149982Z digest=sha256:372421f11727f4234560c5360a8f758bf5e769d34725191e3bb25fe0a9ac711b

Observation dde4ec97-27c2-47d3-870a-803fec23218c · outbound

This paper cites Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-12T05:43:50.154010Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.154010Z digest=sha256:42085dec3c158f81098c617ae030e8e4ec18494dae6e4057db1d108e1ef96263

Observation 08f70b30-89d1-4ca2-8dc1-44c6e2bcb10b · outbound

This paper cites Phishing scams detection in Ethereum transaction network.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Phishing scams detection in Ethereum transaction network

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-12T05:43:50.158427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.158427Z digest=sha256:1bac2480ed3c941db859d8f878335341c21589ab64c0ed358ebba1e7521cee43

Observation 76bc6506-dedb-43f9-b6da-4febdba36b6c · outbound

This paper cites Principal neighbourhood aggregation for graph nets.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Principal neighbourhood aggregation for graph nets

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.988362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.162700Z digest=sha256:af58e33a44ad0801cc30e456369ff277081d257511e768cf36a49845584d1419

Observation 39c58762-16b6-47d0-8ec6-d45c1aed97d1 · outbound

This paper cites Provably powerful graph neural networks for directed multigraphs.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Provably powerful graph neural networks for directed multigraphs

Reference 11

Resolution
verified exact
doi, observed 2026-08-12T05:43:50.363023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.167512Z digest=sha256:20f954a829a2a85aa78095eec947ba4ca9c8efd7548db74af13243337f916389

Observation 34ebc343-d6c6-4091-9c00-fe0e2b16977e · outbound

This paper cites Hypergraph neural networks.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Hypergraph neural networks

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-12T05:43:50.172473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.172473Z digest=sha256:8be76354141cc31094e5158fb6a7ccefa8851e69cd99b58439e29bfcf2683734

Observation 7df3206e-37be-4fb1-9f54-c3f9141f0dfb · outbound

This paper cites an unresolved cited work.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Unresolved cited work

Reference 13

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no resolver link, observed 2026-08-12T05:43:50.177350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.177350Z digest=sha256:c91373b72abccfa8df86eec9859dd3d50ae5c31ba453622c1c5ab3c55477a12f

Observation 5464658d-c638-4ffa-8571-ab2857c013c0 · outbound

This paper cites Bronstein, and Haggai Maron.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Bronstein, and Haggai Maron

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.961537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.181938Z digest=sha256:85281fb100957b7f99231e6c18270a66d6c76273aacfa3082ffe79ab32ac00e3

Observation b1f7b0ec-7e1c-417e-8c92-1c146a18e94d · outbound

This paper cites Fuchs* and Petar Veličković*.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Fuchs* and Petar Veličković*

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.946023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.186192Z digest=sha256:1a07672cccebce2e46374d0da060c242aaf4e91d56b869422e0f88513a3683c1

Observation b82e6254-a25d-4d0d-a5a1-e1e7d2708196 · outbound

This paper cites Schoenholz, Patrick F.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Schoenholz, Patrick F

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.932349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.190758Z digest=sha256:9527b813e69375d1cc019011763e9afd82bf0b7789872cc957225489fb1e95ef

Observation 243e1076-f477-404f-99b1-5ef9e7fb0b7f · outbound

This paper cites Inductive representation learning on large graphs.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Inductive representation learning on large graphs

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.918087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.195120Z digest=sha256:776ab1c5c09800830a4008c45efa4293e5a062410d1e897af19e07e942137197

Observation d3f186bf-126a-43a0-89dc-fc42d2bd4429 · outbound

This paper cites Multilayer feedforward networks are universal approximators.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Multilayer feedforward networks are universal approximators

Reference 18

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unresolved
no resolver link, observed 2026-08-12T05:43:50.199845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.199845Z digest=sha256:d3479d88433d29f09447b5fa005831da6d84eae4a57516f6713dafa3d7e546d7

Observation 9e4ade6a-e0da-4aeb-8605-354d50fc0583 · outbound

This paper cites Unignn: a unified framework for graph and hypergraph neural networks.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Unignn: a unified framework for graph and hypergraph neural networks

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-12T05:43:50.204465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.204465Z digest=sha256:a77be8448e58b30bd8d473917b8c5352460a3218e191ebc51c028dc9d3fa7b96

Observation 507e9190-8125-4344-be69-739d1bf56676 · outbound

This paper cites edGNN: a Simple and Powerful GNN for Directed Labeled Graphs.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations edGNN: a Simple and Powerful GNN for Directed Labeled Graphs

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-12T05:43:50.209457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.209457Z digest=sha256:341a2277f189f8e6bde3a84eba75aca4a76cd92cd08f4a20955eaba22cbecd4e

Observation 6f04dbfd-0315-47e9-964e-9bbcda679e55 · outbound

This paper cites Universal invariant and equivariant graph neural networks.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Universal invariant and equivariant graph neural networks

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.903841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.214525Z digest=sha256:e35de6b4fb693fc7bd2100f4b87bea0a339d7d21a1c1786ef6f8ee33153c7390

Observation 8f5a688d-ffaf-40da-8d23-62ca1d96e399 · outbound

This paper cites Kipf and Max Welling.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Kipf and Max Welling

Reference 22

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unresolved
no resolver link, observed 2026-08-12T05:43:50.219052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.219052Z digest=sha256:1b598665be6406984b6e384eb96682a8b89cc7bc9a1d7d2547afa702c3cbe061

Observation 11f0d29a-a11d-401b-830a-3a619919b24f · outbound

This paper cites Generalised f-mean aggregation for graph neural networks.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Generalised f-mean aggregation for graph neural networks

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.877980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.223700Z digest=sha256:78dd831cc256946ea02c178d1579aee03b3569b44cabf91ecfe0dae93c91fe56

Observation 70110522-4287-47bb-a2a5-d1f358f0143b · outbound

This paper cites What graph neural networks cannot learn: depth vs width.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations What graph neural networks cannot learn: depth vs width

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.863147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.228251Z digest=sha256:e27fafeb7b05939c381cb6914d257276621a08c0144650011a03173849f607f9

Observation e4600b10-0580-448f-a8e3-123591c66763 · outbound

This paper cites Provably powerful graph networks.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Provably powerful graph networks

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.847784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.232920Z digest=sha256:ccd5d31a4db6b47f4b5962c57b9c65ea351c7d9f7d5ca75e07a3771cced889f4

Observation 5edeb333-d507-4931-8c6a-f614b810ef35 · outbound

This paper cites Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-12T05:43:50.237438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.237438Z digest=sha256:a66260baaad15c0bdb35bfe0cc9033d1adc637bddc3c90f2966c4baa1b9d2c32

Observation 421778e4-071f-45ed-b3a1-abb03be6d7a8 · outbound

This paper cites Topology (2nd edn), 2000.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Topology (2nd edn), 2000

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.833193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.241813Z digest=sha256:3b1d6fb74af18f8ecec60c4ac88ba3e6d615022cc873528962642a94706ac810

Observation a042cbed-2b93-4b1a-b71b-dfdfa28a58db · outbound

This paper cites Random features strengthen graph neural networks.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Random features strengthen graph neural networks

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.817039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.247240Z digest=sha256:bc4e9511d96c99b3d308856f242f7a3060d32bcd9e44eaeda4d91966dbb7b69d

Observation b456e2f5-8a14-418a-bada-e6e02d7c236e · outbound

This paper cites Modeling relational data with graph convolutional networks.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Modeling relational data with graph convolutional networks

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-12T05:43:50.251626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.251626Z digest=sha256:e530f1b24abc4045a4188945e44c2bbe60d95153204f3d13d958c2ff5c6fd0ec

Observation a4868c62-6f8d-4328-a40a-23627d225d9e · outbound

This paper cites Adamm: Anomaly detection of attributed multi-graphs with metadata: A unified neural network approach.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Adamm: Anomaly detection of attributed multi-graphs with metadata: A unified neural network approach

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-12T05:43:50.256301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.256301Z digest=sha256:29a32a1d29be7d6210c9146779a0eac24c24adbdfe0fc8555342cab24f30b664

Observation 96cecf91-36b1-45a3-909f-3481f528fd20 · outbound

This paper cites Composition-based multi-relational graph convolutional networks.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Composition-based multi-relational graph convolutional networks

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.792909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.261257Z digest=sha256:09dd7f28a6efad1893db432decdf3f1b5d3066d68def55d66b0c0fe285d858d3

Observation b5f6ada4-93c2-4f46-b321-4a81e2e5fda2 · outbound

This paper cites Veličković, G.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Veličković, G

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.777713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.265775Z digest=sha256:692f2a03e6f53850e5f0e5876571807932538a51a95742464c6380cd66403579

Observation b8bc98f9-df14-4d8a-b9cd-d409a438f8ae · outbound

This paper cites How powerful are graph neural networks? In International Conference on Learning Representations, 2019.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations How powerful are graph neural networks? In International Conference on Learning Representations, 2019

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-12T05:43:50.270366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.270366Z digest=sha256:bdd73bc90d4a00b1bbbc69e4d9c4f95198adcbd04e3316a256e20873b737fd34

Observation fc1706ee-d7d0-4de4-8926-989b562cca91 · outbound

This paper cites Identity-aware graph neural networks.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Identity-aware graph neural networks

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.752738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.274717Z digest=sha256:7a919ddab8fb9eff5e74e3a66996fb5db34e9985b3dd215324b22e57d4abe6e7

Observation ca16123b-a927-4b56-859e-3a9b3ce19f72 · outbound

This paper cites Deep sets.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Deep sets

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-12T05:43:50.278985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.278985Z digest=sha256:0edb4a61d162178c23f4a6dd293ed0e8dbc7577412bea3669f9890d1eeadf22b

Observation dbf98188-73c6-4a08-9e75-d1f1790f90e1 · outbound

This paper cites write newline.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations write newline

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-12T05:43:50.283678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.283678Z digest=sha256:4830b16241a249053c2a4ab8ba681563df19162e7c9118daa4803779343319f8

Observation 9c583442-fc0d-410a-9b1f-d559e0480c7a · outbound

This paper cites @esa (Ref.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations @esa (Ref

Reference 37

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unresolved
no resolver link, observed 2026-08-12T05:43:50.289213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.289213Z digest=sha256:4559cc04dc850ba6ca67f68a12a4b00e9b6b304049a33f71e2ed1a5edcbaa85d

Observation 984d6786-f215-4681-9d5a-e1c850fce86b · outbound

This paper cites an unresolved cited work.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Unresolved cited work

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-12T05:43:50.293810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.293810Z digest=sha256:6ce406cd38a0870447a28745f403be1c7f199ab2c18657637e21a6e9139b4c6c

Observation 194b724c-6337-47db-bd97-4614601a5e29 · outbound

This paper cites an unresolved cited work.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Unresolved cited work

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-12T05:43:50.298140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.298140Z digest=sha256:6e69ae39b9af07573a22572234b72e44431c775e20406d9eb47a49b9324d7756

Pith citing papers

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

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

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

Resolution
unresolved
no resolver link, observed 2026-07-14T14:04:13.095632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:6bd0e99718bd8022c05cec8e71d1cf6d1c1bea1d8c8b63ff50a2f50ca530d746