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Paper Citation Record · LEDGER

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing

As of 10 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2511.11046.

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

pith.paper-citation-record.v1
2511.11046 v3

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T22:23:24.234750Z

measured 31 of 31 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

31 of 31 outbound references displayed

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Outbound references

Observation 9d57ce6a-17fb-4144-b636-751b5de4acbd · outbound

This paper cites Contextualized messages boost graph representations,.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing Contextualized messages boost graph representations,

Reference 1

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Observation d48ab73c-c01e-4789-95c6-14a8d2a34d80 · outbound

This paper cites Semi-supervised classification with graph convolutional networks,.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing Semi-supervised classification with graph convolutional networks,

Reference 2

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Observation 848cb8d9-6822-4c78-902b-1ed98d871913 · outbound

This paper cites The reduction of a graph to a canon- ical form and an algebra arising during this reduction,.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing The reduction of a graph to a canon- ical form and an algebra arising during this reduction,

Reference 3

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Observation e6498a47-802a-4b1f-90ea-25dd911e7348 · outbound

This paper cites How powerful are graph neural networks?.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing How powerful are graph neural networks?

Reference 4

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Observation f3f21bd0-17bb-4911-990b-dd3759ce6bf4 · outbound

This paper cites Random features strengthen graph neural networks,.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing Random features strengthen graph neural networks,

Reference 5

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Observation 6224dbed-5a03-4c58-acd6-bedd4233d162 · outbound

This paper cites DeeperGCN: All You Need to Train Deeper GCNs.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing DeeperGCN: All You Need to Train Deeper GCNs

Reference 6

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Observation 11fdca0e-1c5d-4e85-9f5f-5af5f6f35234 · outbound

This paper cites GraphSAGE-based traffic speed forecasting for segment network with sparse data,.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing GraphSAGE-based traffic speed forecasting for segment network with sparse data,

Reference 7

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Observation db96393f-22fa-4cdb-afff-4fdbfc432ea7 · outbound

This paper cites Understanding graph isomorphism network for rs-fMRI functional connectivity analysis,.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing Understanding graph isomorphism network for rs-fMRI functional connectivity analysis,

Reference 8

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Observation 80f3f243-d565-473b-8b8d-e95b30dbe49a · outbound

This paper cites Benchmarking graph neural networks,.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing Benchmarking graph neural networks,

Reference 9

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Observation 89d84adb-a6aa-4288-8193-2d17c0648660 · outbound

This paper cites Open graph benchmark: Datasets for machine learning on graphs,.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing Open graph benchmark: Datasets for machine learning on graphs,

Reference 10

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Observation 7e911e81-a18a-44ab-9a75-220fd856a348 · outbound

This paper cites Graph attention networks,.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing Graph attention networks,

Reference 11

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Observation c96d7b2c-73c4-412a-8564-2a86c4003560 · outbound

This paper cites How attentive are graph attention networks?.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing How attentive are graph attention networks?

Reference 12

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Observation 39b94f21-0baa-468a-801e-9e7e298a7ac8 · outbound

This paper cites EGAT: Edge-featured graph attention network,.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing EGAT: Edge-featured graph attention network,

Reference 13

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Observation 9c67ab28-65c4-4e3c-96b0-4a39b25eb449 · outbound

This paper cites FinGAT: Financial graph attention networks for recommending top-k profitable stocks,.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing FinGAT: Financial graph attention networks for recommending top-k profitable stocks,

Reference 14

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Observation 583c325a-0922-4f53-9a2c-ef4debc88171 · outbound

This paper cites GATrust: A multi-aspect graph attention network model for trust assessment in OSNs,.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing GATrust: A multi-aspect graph attention network model for trust assessment in OSNs,

Reference 15

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Observation 285b03e8-ef89-4fcc-9a34-37f735f948a7 · outbound

This paper cites FinSIR: Financial SIR-GCN for market-aware stock recommendation,.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing FinSIR: Financial SIR-GCN for market-aware stock recommendation,

Reference 16

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Observation 7f119454-e698-4baf-b009-b325b034f06b · outbound

This paper cites AGTCNet: A graph-temporal approach for principled motor imagery EEG classification,.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing AGTCNet: A graph-temporal approach for principled motor imagery EEG classification,

Reference 17

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Observation 9fdfab10-e003-4518-bf84-b82ed13823f8 · outbound

This paper cites Neural message passing for quantum chemistry,.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing Neural message passing for quantum chemistry,

Reference 18

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Observation f3cc1a07-8dd7-4bd8-b0bb-5bade9ce700f · outbound

This paper cites Principal neighbourhood aggregation for graph nets,.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing Principal neighbourhood aggregation for graph nets,

Reference 19

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Observation a58c5d9c-4e92-4e37-b078-bd7661376a7a · outbound

This paper cites Do we need anisotropic graph neural networks?.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing Do we need anisotropic graph neural networks?

Reference 20

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Observation 013db0b3-6b3d-4c24-b687-66dcd688c0e2 · outbound

This paper cites Weisfeiler and leman go neural: higher-order graph neu- ral networks,.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing Weisfeiler and leman go neural: higher-order graph neu- ral networks,

Reference 21

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Observation 9a58cea3-f9f4-41f4-bfaf-bc4994ba0bc6 · outbound

This paper cites Expressive power of invariant and equiv- ariant graph neural networks,.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing Expressive power of invariant and equiv- ariant graph neural networks,

Reference 22

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Observation 7deacb19-4c80-4a14-bfe8-dd483e597c0f · outbound

This paper cites Weisfeiler and Lehman go cellular: CW networks,.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing Weisfeiler and Lehman go cellular: CW networks,

Reference 23

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Observation fd888136-6517-445f-8945-c59a43e52a28 · outbound

This paper cites Do transformers really perform badly for graph representation?.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing Do transformers really perform badly for graph representation?

Reference 24

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Observation 27e4c163-dd7f-4986-bdaf-15072701b72e · outbound

This paper cites Adaptive Graph Diffusion Networks.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing Adaptive Graph Diffusion Networks

Reference 25

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Observation 074f0e39-b914-4530-a103-3cee58dd714f · outbound

This paper cites Improving graph neural network expressivity via subgraph isomorphism count- ing,.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing Improving graph neural network expressivity via subgraph isomorphism count- ing,

Reference 26

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Observation 30413ef1-e91c-41a2-baa4-6a999a10100f · outbound

This paper cites Towards expressive graph representations for graph neural networks,.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing Towards expressive graph representations for graph neural networks,

Reference 27

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Observation 0a24e078-4c99-454e-a12d-df3b09e3e181 · outbound

This paper cites Towards dynamic mes- sage passing on graphs,.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing Towards dynamic mes- sage passing on graphs,

Reference 28

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Observation a273953c-7c81-4b7b-999f-8197f93949c7 · outbound

This paper cites A simple and expressive graph neural network based method for structural link rep- resentation,.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing A simple and expressive graph neural network based method for structural link rep- resentation,

Reference 29

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Observation 37d10170-3de5-483d-89ac-0e1657947c5e · outbound

This paper cites Neural execution of graph algorithms,.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing Neural execution of graph algorithms,

Reference 30

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Observation 62175070-2971-4ec4-9c99-d3e14356d36b · outbound

This paper cites an unresolved cited work.

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing Unresolved cited work

Reference 2021

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