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Paper Citation Record · LEDGER

Resolving Oversmoothing with Opinion Dissensus

As of 10 August 2026, this Paper Citation Record lists 95 of 95 outbound references and 2 inbound Pith citation observations for arXiv:2501.19089.

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

pith.paper-citation-record.v1
2501.19089 v2

Coverage vector

measured 95 of 95 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T21:29:28.147348Z

measured 97 of 97 standing notices

One-hop event checks from named stored sources.

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

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T21:43:50.425136Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T21:45:05.589364Z

Reference resolution

95 of 95 outbound references displayed

  • verified exact3
  • verified fuzzy42
  • unresolved50
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bfc100ea-cfad-40a5-8a3a-7f50655f857b · outbound

This paper cites Geometry-enhanced molecular representation learning for property prediction.

Resolving Oversmoothing with Opinion Dissensus Geometry-enhanced molecular representation learning for property prediction

Reference 1

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Observation 2795cb7b-4586-4a6b-8df1-80edcf735e56 · outbound

This paper cites Starling flock networks manage uncertainty in consensus at low cost.

Resolving Oversmoothing with Opinion Dissensus Starling flock networks manage uncertainty in consensus at low cost

Reference 2

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Observation e30ceb14-da89-4338-b80d-93481e78bbb0 · outbound

This paper cites A latency-defined edge node placement scheme for opportunistic smart cities.

Resolving Oversmoothing with Opinion Dissensus A latency-defined edge node placement scheme for opportunistic smart cities

Reference 3

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Observation a9b8c2eb-b214-4bc1-84e0-3ecbb78b8b72 · outbound

This paper cites Graph convolutional neural networks for web-scale recommender systems.

Resolving Oversmoothing with Opinion Dissensus Graph convolutional neural networks for web-scale recommender systems

Reference 4

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Observation 9c663ab4-2e69-4bde-b067-df1d49047ba0 · outbound

This paper cites Molecular contrastive learning of representations via graph neural networks.

Resolving Oversmoothing with Opinion Dissensus Molecular contrastive learning of representations via graph neural networks

Reference 5

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Observation 74f57603-17db-4a0b-9e76-46c403ffc600 · outbound

This paper cites Prediction of protein–protein interaction using graph neural networks.

Resolving Oversmoothing with Opinion Dissensus Prediction of protein–protein interaction using graph neural networks

Reference 6

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Observation 4eaa404a-b251-4ee6-ae1b-26c5e97e776f · outbound

This paper cites Deeprank-gnn: a graph neural network framework to learn patterns in protein–protein interfaces.

Resolving Oversmoothing with Opinion Dissensus Deeprank-gnn: a graph neural network framework to learn patterns in protein–protein interfaces

Reference 7

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Observation 02327d1b-fe8f-440f-a701-ba29eacee74a · outbound

This paper cites A graph placement methodology for fast chip design.

Resolving Oversmoothing with Opinion Dissensus A graph placement methodology for fast chip design

Reference 8

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Observation c91d30e4-09ee-4697-a4ed-8275ab66a4c8 · outbound

This paper cites Deeper insights into graph convolutional networks for semi-supervised learning.

Resolving Oversmoothing with Opinion Dissensus Deeper insights into graph convolutional networks for semi-supervised learning

Reference 9

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Observation 17c27ff3-07bf-430f-97ac-cbd49dc8b0c7 · outbound

This paper cites Graph Neural Networks Exponentially Lose Expressive Power for Node Classification.

Resolving Oversmoothing with Opinion Dissensus Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 10

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Observation f09bc8fb-60b9-4d3f-8f25-4cec48b2c8b5 · outbound

This paper cites Revisiting Graph Neural Networks: All We Have is Low-Pass Filters.

Resolving Oversmoothing with Opinion Dissensus Revisiting Graph Neural Networks: All We Have is Low-Pass Filters

Reference 11

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Observation a95139ff-9a79-4009-929a-39a43955f238 · outbound

This paper cites Simple and deep graph convolutional networks.

Resolving Oversmoothing with Opinion Dissensus Simple and deep graph convolutional networks

Reference 12

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Observation 7ad0537b-3047-4515-824d-72d8d43a85fd · outbound

This paper cites Towards deeper graph neural networks.

Resolving Oversmoothing with Opinion Dissensus Towards deeper graph neural networks

Reference 13

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Observation 647ee64d-db83-4af0-9ffc-cfd17f39bead · outbound

This paper cites InInternational conference on machine learning, pages 6878–6917.

Resolving Oversmoothing with Opinion Dissensus InInternational conference on machine learning, pages 6878–6917

Reference 14

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Observation 6a4d0841-e34f-4910-a412-8d13256bb1c3 · outbound

This paper cites PairNorm: Tackling Oversmoothing in GNNs.

Resolving Oversmoothing with Opinion Dissensus PairNorm: Tackling Oversmoothing in GNNs

Reference 15

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Observation 9b77112b-9252-4025-b061-6bc4a5cf1801 · outbound

This paper cites Towards deeper graph neural networks with differentiable group normalization.

Resolving Oversmoothing with Opinion Dissensus Towards deeper graph neural networks with differentiable group normalization

Reference 16

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Observation bc25cebf-95ee-475f-8e31-a218b51d4f48 · outbound

This paper cites Understanding and resolving performance degradation in deep graph convolutional networks.

Resolving Oversmoothing with Opinion Dissensus Understanding and resolving performance degradation in deep graph convolutional networks

Reference 17

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Observation 64b9f826-ce78-4f9c-abac-208753281850 · outbound

This paper cites Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks.

Resolving Oversmoothing with Opinion Dissensus Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks

Reference 18

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Observation d7fb83a0-b749-485a-ab68-8493f039ca3c · outbound

This paper cites Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing.

Resolving Oversmoothing with Opinion Dissensus Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing

Reference 19

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Observation 2bd85920-8f49-477a-8ff4-7c571f927aeb · outbound

This paper cites GraphSAINT: Graph Sampling Based Inductive Learning Method.

Resolving Oversmoothing with Opinion Dissensus GraphSAINT: Graph Sampling Based Inductive Learning Method

Reference 20

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Observation 9edcc989-f84f-4741-bf4b-a8c2eed52a4d · outbound

This paper cites Adaptive Universal Generalized PageRank Graph Neural Network.

Resolving Oversmoothing with Opinion Dissensus Adaptive Universal Generalized PageRank Graph Neural Network

Reference 21

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Observation 0cfde880-d6b6-41f3-942c-1dd25d94ab2e · outbound

This paper cites Beltrami flow and neural diffusion on graphs.

Resolving Oversmoothing with Opinion Dissensus Beltrami flow and neural diffusion on graphs

Reference 22

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 5a5dae01-b17b-429d-b5ea-bc3dc0b36908 · outbound

This paper cites Clifford Group Equivariant Simplicial Message Passing Networks.

Resolving Oversmoothing with Opinion Dissensus Clifford Group Equivariant Simplicial Message Passing Networks

Reference 23

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Observation 4023c853-53d9-44c2-a939-6988916899cd · outbound

This paper cites A generalized neural diffusion framework on graphs.

Resolving Oversmoothing with Opinion Dissensus A generalized neural diffusion framework on graphs

Reference 24

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Observation 774bc073-ef7f-4995-884e-83e41acff324 · outbound

This paper cites Graph Neural Ordinary Differential Equations.

Resolving Oversmoothing with Opinion Dissensus Graph Neural Ordinary Differential Equations

Reference 25

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Observation eee75488-1f6a-4e06-bb1d-b442056edfc5 · outbound

This paper cites Grand: Graph neural diffusion.

Resolving Oversmoothing with Opinion Dissensus Grand: Graph neural diffusion

Reference 26

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Observation 931fc1a3-4f49-4f04-824f-c025a02db2fa · outbound

This paper cites Pde-gcn: Novel architectures for graph neural networks motivated by partial differential equations.

Resolving Oversmoothing with Opinion Dissensus Pde-gcn: Novel architectures for graph neural networks motivated by partial differential equations

Reference 27

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Observation 1ea44700-e0ca-4ba6-8666-98aa1baf8b12 · outbound

This paper cites Nonlinear dynamics and chaos.

Resolving Oversmoothing with Opinion Dissensus Nonlinear dynamics and chaos

Reference 28

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 84196646-299b-43f7-b7f0-f1be2016c215 · outbound

This paper cites Discovering governing equations from data by sparse identification of nonlinear dynamical systems.

Resolving Oversmoothing with Opinion Dissensus Discovering governing equations from data by sparse identification of nonlinear dynamical systems

Reference 29

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Observation e55e311e-c313-43e3-86fc-64ca3eaefc74 · outbound

This paper cites Output-only identification of self- excited systems using discrete-time lur’e models with application to a gas-turbine combustor.

Resolving Oversmoothing with Opinion Dissensus Output-only identification of self- excited systems using discrete-time lur’e models with application to a gas-turbine combustor

Reference 30

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verified fuzzy
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Observation c17201ee-98fd-49f6-8a23-c6865935e7a6 · outbound

This paper cites Output-only identi- fication of lur’e systems with hysteretic feedback nonlinearities.

Resolving Oversmoothing with Opinion Dissensus Output-only identi- fication of lur’e systems with hysteretic feedback nonlinearities

Reference 31

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verified fuzzy
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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation d913c109-75c1-4b3e-896e-56af033ba171 · outbound

This paper cites Graph-coupled oscillator networks.

Resolving Oversmoothing with Opinion Dissensus Graph-coupled oscillator networks

Reference 32

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Observation da7b4a58-f86f-4778-9771-1852c96ac188 · outbound

This paper cites ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networks.

Resolving Oversmoothing with Opinion Dissensus ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networks

Reference 33

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verified exact
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Observation be891aba-55da-4395-b96c-cb9a700d41d0 · outbound

This paper cites From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond.

Resolving Oversmoothing with Opinion Dissensus From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond

Reference 34

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Observation 9fb6f361-1539-4146-96df-e8f924a3586e · outbound

This paper cites Fast and flexible multiagent decision-making.

Resolving Oversmoothing with Opinion Dissensus Fast and flexible multiagent decision-making

Reference 35

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verified fuzzy
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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation ceb41b83-67d5-4867-ad38-8d5e1644568c · outbound

This paper cites Nonlinear opinion dynamics with tunable sensitivity.

Resolving Oversmoothing with Opinion Dissensus Nonlinear opinion dynamics with tunable sensitivity

Reference 36

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 9ad1a85b-0591-4b73-b274-9fae72767c6b · outbound

This paper cites On the number of linear regions of deep neural networks.

Resolving Oversmoothing with Opinion Dissensus On the number of linear regions of deep neural networks

Reference 37

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation c8ed424b-98fe-480f-857a-19420ebbc218 · outbound

This paper cites Deep learning.

Resolving Oversmoothing with Opinion Dissensus Deep learning

Reference 38

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no resolver link, observed 2026-08-09T21:29:27.887699Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T21:29:27.887699Z digest=sha256:af3048e60f1e71f22d7ee6947857ca30848973759fee9820a6df828894d20312

Observation b9818c97-630e-45f3-b6df-d5b95666e21b · outbound

This paper cites A Non-Asymptotic Analysis of Oversmoothing in Graph Neural Networks.

Resolving Oversmoothing with Opinion Dissensus A Non-Asymptotic Analysis of Oversmoothing in Graph Neural Networks

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-09T21:29:27.891714Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T21:29:27.891714Z digest=sha256:c40c8f49e220d2c088072aed3e38e62b96033a85a852701a8a3e834cb9048144

Observation 5d097e60-1330-47d0-933f-68aefe0ce044 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Resolving Oversmoothing with Opinion Dissensus Semi-Supervised Classification with Graph Convolutional Networks

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-09T21:29:27.896756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T21:29:27.896756Z digest=sha256:af199997c7e1f19b60fbff836924bb94f3b6df1fb9930cab149e9989be072a2a

Observation 482f5cb3-2d38-471f-a23a-73a18bad5838 · outbound

This paper cites Revisiting Over-smoothing in Deep GCNs.

Resolving Oversmoothing with Opinion Dissensus Revisiting Over-smoothing in Deep GCNs

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-09T21:29:27.901311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T21:29:27.901311Z digest=sha256:4b47128dfeb84d6b6c547a2e22eea244032f8eb6b5f7612ad6c5b2b438b5470e

Observation 74c8de23-b7b6-40c2-8aeb-71939af6f9ec · outbound

This paper cites Graph Attention Networks.

Resolving Oversmoothing with Opinion Dissensus Graph Attention Networks

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-09T21:29:27.906257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T21:29:27.906257Z digest=sha256:97f3c03841a5540a58aacb6576377c53f6ddfab46941dc7b10099a12e5c858fc

Observation 4b5dacfd-8362-42b0-9bd4-195d03c09299 · outbound

This paper cites Demystifying oversmoothing in attention-based graph neural networks.

Resolving Oversmoothing with Opinion Dissensus Demystifying oversmoothing in attention-based graph neural networks

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T21:29:29.287741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:27.911053Z digest=sha256:4d4a4d6dedff5407263bce06b603ea6a0155a30b6c6e84ab024cc66c38c1da10

Observation 6cf2c913-eb5d-42d7-8e7e-fc7513a46210 · outbound

This paper cites Understanding convolution on graphs via energies.

Resolving Oversmoothing with Opinion Dissensus Understanding convolution on graphs via energies

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-09T21:29:27.915677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T21:29:27.915677Z digest=sha256:562a39f51a6fd4796c3c367d12fef229ec0f67b4512e2a8c0748cc0241d5380d

Observation bd28229d-bfcd-48cc-8c18-b767cbe808d1 · outbound

This paper cites Preventing Representational Rank Collapse in MPNNs by Splitting the Computational Graph.

Resolving Oversmoothing with Opinion Dissensus Preventing Representational Rank Collapse in MPNNs by Splitting the Computational Graph

Reference 45

Resolution
verified exact
local_arxiv, observed 2026-08-09T21:29:28.508889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:27.920112Z digest=sha256:5b38d053fdaca32e56702452f2cf413bafa4c4db227c922d1d1a4d1235a3d66b

Observation dfba2b76-076d-4f72-9634-4eac05036e8e · outbound

This paper cites Not too little, not too much: a theoretical analysis of graph (over) smoothing.

Resolving Oversmoothing with Opinion Dissensus Not too little, not too much: a theoretical analysis of graph (over) smoothing

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T21:29:29.272747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:27.925045Z digest=sha256:b0d209053c46b325c09b407949bae959898a968836f7354ab9b2b85574b9cb78

Observation 4542c6e9-3ebb-4262-b33b-e393adad54ba · outbound

This paper cites Neural ordinary differential equations.

Resolving Oversmoothing with Opinion Dissensus Neural ordinary differential equations

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-09T21:29:27.929615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T21:29:27.929615Z digest=sha256:df0ac536c0443faf8434d08dfe26dc64e3cb2ae32ce98a55e72a9a94f570e387

Observation 5b2d5ca9-24c7-442f-9a2d-eb8d8b864710 · outbound

This paper cites Grand++: Graph neural diffusion with a source term.

Resolving Oversmoothing with Opinion Dissensus Grand++: Graph neural diffusion with a source term

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T21:29:29.249613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:27.934524Z digest=sha256:9c0336adfefb0f3d293bf99ddd338d8629124d795ec2bbb5a29b2368488bc2bd

Observation 4b1dc8eb-824c-4fae-99b3-20779ba9b68c · outbound

This paper cites Gread: Graph neural reaction-diffusion networks.

Resolving Oversmoothing with Opinion Dissensus Gread: Graph neural reaction-diffusion networks

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T21:29:29.235556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:27.938574Z digest=sha256:da60a0ed3bc1d1a2cb78e27b0bed1dcc81f4f6bf70606f455ca909db4c23be81

Observation 5c717ec6-1d45-4e53-a077-91f265df992e · outbound

This paper cites From coupled oscillators to graph neural networks: Reducing over-smoothing via a kuramoto model-based approach.

Resolving Oversmoothing with Opinion Dissensus From coupled oscillators to graph neural networks: Reducing over-smoothing via a kuramoto model-based approach

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T21:29:29.221048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:27.943397Z digest=sha256:c98f349a986428a16c51f10c490f2d61e3a6dc64a971c5208f54347428140593

Observation 19c3c4fa-7c07-48d8-a9c8-fac9ed1fc7a8 · outbound

This paper cites Graph neural ricci flow: Evolving feature from a curvature perspective.

Resolving Oversmoothing with Opinion Dissensus Graph neural ricci flow: Evolving feature from a curvature perspective

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T21:29:29.207185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:27.947716Z digest=sha256:5ba939b00d94381d51d31290a96eb1948dd1dfe95ff99df64d6a05bae66e6625

Observation 72bf67ac-f88a-49d0-b36a-138f40a2f178 · outbound

This paper cites Lectures on network systems, volume 1.

Resolving Oversmoothing with Opinion Dissensus Lectures on network systems, volume 1

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T21:29:29.192782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:27.952349Z digest=sha256:3f474c4da3f3d1dd0be004db88e6fd7007a7017a13601a9e7efebd868454d009

Observation f33221a8-c737-4387-b72d-87bb35b64688 · outbound

This paper cites Natural frames and interacting particles in three dimensions.

Resolving Oversmoothing with Opinion Dissensus Natural frames and interacting particles in three dimensions

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T21:29:29.178330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:27.956488Z digest=sha256:9a8da75f27c9d91581eddede4f4d379421dd3165d9280a7d6b497c308a9f9e7f

Observation 5fd8261c-7140-4a66-895a-337286111403 · outbound

This paper cites Coordinated control of an underwater glider fleet in an adaptive ocean sampling field experiment in monterey bay.

Resolving Oversmoothing with Opinion Dissensus Coordinated control of an underwater glider fleet in an adaptive ocean sampling field experiment in monterey bay

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T21:29:29.163679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:27.961569Z digest=sha256:8586cab981025223eefb5c381d44dd3e63c6f93a3f9ffa3dfef55ff765e563b0

Observation 0b1b42f9-5181-4139-8c67-8e42e5470d9b · outbound

This paper cites Collective motion, sensor networks, and ocean sampling.

Resolving Oversmoothing with Opinion Dissensus Collective motion, sensor networks, and ocean sampling

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T21:29:29.148006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:27.965773Z digest=sha256:21a4a9c33e670556d6e94ddb6f3e404a8f887b8986b41163905861dc10387b8e

Observation b3d93279-6721-4c11-8347-a0e29ea75a40 · outbound

This paper cites Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study.

Resolving Oversmoothing with Opinion Dissensus Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T21:29:29.133363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:27.970617Z digest=sha256:8eee7857f46783dddfdc29b97277e9cc23352ea3dacf070db844dff54c0b47b9

Observation 5b2c751b-77a2-42ab-9462-c83b31ea0bf1 · outbound

This paper cites Odnet: Opinion dynamics-inspired neural message passing for graphs and hypergraphs.Transactions on Machine Learning Research, 2024.

Resolving Oversmoothing with Opinion Dissensus Odnet: Opinion dynamics-inspired neural message passing for graphs and hypergraphs.Transactions on Machine Learning Research, 2024

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T21:29:29.118970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:27.974823Z digest=sha256:21213ce31603a4f92d1abd661b238772ba074c3e2e1cfd87360854fdc28b0e98

Observation 079ca40c-0343-43aa-9c82-df7634333f38 · outbound

This paper cites Consensus problems on networks with antagonistic interactions.

Resolving Oversmoothing with Opinion Dissensus Consensus problems on networks with antagonistic interactions

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T21:29:29.104075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:27.978842Z digest=sha256:f98f907e287dbee5af3f189cf79b30635463c0b80ade6d36fa15e0c05730d07e

Observation f0ac6e61-c44c-4590-a891-961baa778760 · outbound

This paper cites Biased assimilation, homophily, and the dynamics of polarization.

Resolving Oversmoothing with Opinion Dissensus Biased assimilation, homophily, and the dynamics of polarization

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T21:29:29.089830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:27.983872Z digest=sha256:d9221a272b92ac952ab8bc978c3386e05c1c09aa961c697a5de103bc5e9e0150

Observation 0cc1551e-d991-4c6e-a694-73129edc428b · outbound

This paper cites Springer Science & Business Media, 2012.

Resolving Oversmoothing with Opinion Dissensus Springer Science & Business Media, 2012

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-09T21:29:27.988098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T21:29:27.988098Z digest=sha256:ce2225b87d770fed454c7b41048ebfc27fab528052858c904134d3c9ef6b5252

Observation b95fec96-3a7e-4ddc-9caa-f1d87f31602b · outbound

This paper cites The nonlinear feedback dynamics of asymmetric political polarization.

Resolving Oversmoothing with Opinion Dissensus The nonlinear feedback dynamics of asymmetric political polarization

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T21:29:29.066504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:27.992173Z digest=sha256:1b0d8610a565ac657d1e35308254f0236804043be26ccfe3cc87a140baf5bdb6

Observation 1eb5fa15-1abc-419d-8c7a-46784788479e · outbound

This paper cites Active risk aversion in sis epidemics on networks.

Resolving Oversmoothing with Opinion Dissensus Active risk aversion in sis epidemics on networks

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T21:29:29.051853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:27.997142Z digest=sha256:ee02b7d68ba1bc7245b23fd590f5c1231c0c3a03568a24484497e1d1525f5bfd

Observation 466c9eaf-e7c6-4821-b909-3dfe23c039d3 · outbound

This paper cites Opinion-driven risk perception and reaction in SIS epidemics.

Resolving Oversmoothing with Opinion Dissensus Opinion-driven risk perception and reaction in SIS epidemics

Reference 63

Resolution
unresolved
no resolver link, observed 2026-08-09T21:29:28.001349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T21:29:28.001349Z digest=sha256:e31f1aa96c473b2759a3137acbbe4148857eb66e7ebfefc2f0dc7294675f59aa

Observation af1716a5-9163-4606-ac88-262490ca1afc · outbound

This paper cites Opinion dynamics for decentralized decision-making in a robot swarm.

Resolving Oversmoothing with Opinion Dissensus Opinion dynamics for decentralized decision-making in a robot swarm

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T21:29:29.037966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:28.006636Z digest=sha256:eedeaff34adb6621a7abbeeebae0a24ac418b03705326ea6a99726da27aa09d0

Observation 6b7f280d-4277-44fa-9acf-e68de7546f3f · outbound

This paper cites Behavior-Inspired Neural Networks for Relational Inference.

Resolving Oversmoothing with Opinion Dissensus Behavior-Inspired Neural Networks for Relational Inference

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-09T21:29:28.010734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T21:29:28.010734Z digest=sha256:d1682006f0d645a7099472a93bdaff5c36c7a378be2e07f60958cddf96bae60a

Observation 4f41e553-3104-467d-8911-9f347b3586e5 · outbound

This paper cites Neural message passing for quantum chemistry.

Resolving Oversmoothing with Opinion Dissensus Neural message passing for quantum chemistry

Reference 66

Resolution
unresolved
no resolver link, observed 2026-08-09T21:29:28.015812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T21:29:28.015812Z digest=sha256:eda332926511a6780e16e65058369d5ae4ccec65d42b98c4175fc835d3b6374e

Observation 118c5610-37cf-4c97-a86c-74b054c6dba1 · outbound

This paper cites A Survey on Oversmoothing in Graph Neural Networks.

Resolving Oversmoothing with Opinion Dissensus A Survey on Oversmoothing in Graph Neural Networks

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-09T21:29:28.020175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T21:29:28.020175Z digest=sha256:653869de96769d23587edf8b26cf8d26339e99be7091935a69ffdec342b26918

Observation 282fb88e-e8cc-431c-9f79-4e3a6e6af4e8 · outbound

This paper cites Continuous graph neural networks.

Resolving Oversmoothing with Opinion Dissensus Continuous graph neural networks

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T21:29:29.015599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:28.024907Z digest=sha256:9fa0d4228558e3d6077cea548805a864430265c1045020f8831126dfb05d70c9

Observation 7c1dd11d-f251-450a-be81-7e53bbfc13bf · outbound

This paper cites A Note on Over-Smoothing for Graph Neural Networks.

Resolving Oversmoothing with Opinion Dissensus A Note on Over-Smoothing for Graph Neural Networks

Reference 69

Resolution
unresolved
no resolver link, observed 2026-08-09T21:29:28.029182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T21:29:28.029182Z digest=sha256:f65c857c46d1f4b199668baa2a39bb3cea5f9ce94393a3dfdac89228f368ad65

Observation 37be1723-af6a-46a1-a646-e1d07d378194 · outbound

This paper cites Reaching a consensus.

Resolving Oversmoothing with Opinion Dissensus Reaching a consensus

Reference 70

Resolution
unresolved
no resolver link, observed 2026-08-09T21:29:28.034300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T21:29:28.034300Z digest=sha256:29b0879b8595ddde423a9ac1abda1379896954638ce5af0bd81d7d5ed270eef6

Observation 90f43340-cc6b-415f-a53b-a12b081fb941 · outbound

This paper cites an unresolved cited work.

Resolving Oversmoothing with Opinion Dissensus Unresolved cited work

Reference 71

Resolution
unresolved
raw_fallback, observed 2026-08-09T21:29:28.991673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:28.038412Z digest=sha256:f2a312031b6fc331ab0cadd0cd0a78c8fc40bb911ece769ef9e2efb01c3a2c6f

Observation 9bee0713-195d-442f-bde4-683ded0b1520 · outbound

This paper cites On convergence rate of weighted-averaging dynamics for consensus problems.

Resolving Oversmoothing with Opinion Dissensus On convergence rate of weighted-averaging dynamics for consensus problems

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T21:29:28.977691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:28.042595Z digest=sha256:3961187286a220c9a8f6882f0425dbc3b8e3c07f8ec36228eacf6e591277dda9

Observation eb61b131-b08e-4428-92e6-0f6b8c747884 · outbound

This paper cites Information flow and cooperative control of vehicle formations.

Resolving Oversmoothing with Opinion Dissensus Information flow and cooperative control of vehicle formations

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T21:29:28.963433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:28.046573Z digest=sha256:e13097be16e536299f5b6ff9591737cb4d9c1b710d558c47f011a4b0d950c15c

Observation 5d0cf567-9e4b-41c2-aba6-07d406d485c3 · outbound

This paper cites Convergence in multiagent coordination, consensus, and flocking.

Resolving Oversmoothing with Opinion Dissensus Convergence in multiagent coordination, consensus, and flocking

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T21:29:28.948959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:28.050623Z digest=sha256:24266541c510e9a87e4be902a2c1f9d8a6e376a7671e41f12adb002401959c68

Observation 288e1128-2fa4-42a0-bb2a-f8782a53b66b · outbound

This paper cites Proactive opinion- driven robot navigation around human movers.

Resolving Oversmoothing with Opinion Dissensus Proactive opinion- driven robot navigation around human movers

Reference 75

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T21:29:28.933612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:28.054921Z digest=sha256:477da4beb85adc6102406f8d8785e447b6eb965798ca6de3ff655af77e5903aa

Observation d4b087d7-c175-41ab-8e1b-2f283a2fdc1c · outbound

This paper cites Simplifying graph convolutional networks.

Resolving Oversmoothing with Opinion Dissensus Simplifying graph convolutional networks

Reference 76

Resolution
unresolved
no resolver link, observed 2026-08-09T21:29:28.059664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T21:29:28.059664Z digest=sha256:17e78b8cd3c3046455f066caf5126ca85241f6f037bf964de17aae4f64163d4b

Observation d04bf323-9c9e-48cb-9666-f2f95e98621b · outbound

This paper cites Dissecting the diffusion process in linear graph convolutional networks.

Resolving Oversmoothing with Opinion Dissensus Dissecting the diffusion process in linear graph convolutional networks

Reference 77

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:28.064014Z digest=sha256:ab2052ccd21af9b577229ca0b07532c3dff32b4ffd139d61048fe91d6702bcd7

Observation 7be01041-2889-464a-ba2a-6b5cb4296860 · outbound

This paper cites Graph Neural Convection-Diffusion with Heterophily.

Resolving Oversmoothing with Opinion Dissensus Graph Neural Convection-Diffusion with Heterophily

Reference 78

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local_arxiv, observed 2026-08-09T21:29:28.427794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:28.068809Z digest=sha256:237a4df2003cded0f39c931c6caec290351a0865ad575ea8f42cde6fa8a297b1

Observation 29f7ea65-85c3-48fd-a2ae-3930ba45f87c · outbound

This paper cites Feature Transportation Improves Graph Neural Networks.

Resolving Oversmoothing with Opinion Dissensus Feature Transportation Improves Graph Neural Networks

Reference 79

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source=pdf_text observed=2026-08-09T21:29:28.073260Z digest=sha256:53bcaf8d8e4d4ce19ed9a5808cee2f86b2de100666881ac1afb25616eb2954b9

Observation 089cbd12-284e-45a9-8d6c-611c92da7706 · outbound

This paper cites On the difficulty of training recurrent neural networks.

Resolving Oversmoothing with Opinion Dissensus On the difficulty of training recurrent neural networks

Reference 80

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source=pdf_text observed=2026-08-09T21:29:28.078400Z digest=sha256:d62bf25140013c147b39ca1e8fdec459838fac04e23376d05352c3ac256bbed1

Observation d6b286c3-ddba-4491-8275-dbf932c3964f · outbound

This paper cites A convergence analysis of gradient descent on graph neural networks.

Resolving Oversmoothing with Opinion Dissensus A convergence analysis of gradient descent on graph neural networks

Reference 81

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raw_fallback, observed 2026-08-09T21:29:28.885614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:28.083360Z digest=sha256:a8bed65e51691b75588f68e17cefd118f3093d24e6f5690124706848db9b0450

Observation f67d3d3e-6ce5-4ce5-bb6e-7885642f4a76 · outbound

This paper cites On vanishing gradients, over- smoothing, and over-squashing in gnns: Bridging recurrent and graph learning.

Resolving Oversmoothing with Opinion Dissensus On vanishing gradients, over- smoothing, and over-squashing in gnns: Bridging recurrent and graph learning

Reference 82

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source=pdf_text observed=2026-08-09T21:29:28.088211Z digest=sha256:e2c0ac558757ea2086e0531d9cb7d10f44d61272b33f570df2a554b0df79d102

Observation 822887e8-d40b-43c5-885a-cfbeca37f151 · outbound

This paper cites Beyond low-frequency information in graph convolutional networks.

Resolving Oversmoothing with Opinion Dissensus Beyond low-frequency information in graph convolutional networks

Reference 83

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verified fuzzy
raw_fallback, observed 2026-08-09T21:29:28.871190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:28.092458Z digest=sha256:1842849ca1c9c146571f495d3d262eefadb2022d84988b8040068e3533ce6f8b

Observation 606131b2-2f0c-459f-be5c-9a5436149ec4 · outbound

This paper cites Analyzing the expressive power of graph neural networks in a spectral perspective.

Resolving Oversmoothing with Opinion Dissensus Analyzing the expressive power of graph neural networks in a spectral perspective

Reference 84

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raw_fallback, observed 2026-08-09T21:29:28.855513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:28.097141Z digest=sha256:f3b362d4c69d8083698e0969654d8bff7834155207979649d9b217297248746d

Observation 0698c5e3-464f-44e7-a632-5efa632b275f · outbound

This paper cites Automating the construction of internet portals with machine learning.

Resolving Oversmoothing with Opinion Dissensus Automating the construction of internet portals with machine learning

Reference 85

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source=pdf_text observed=2026-08-09T21:29:28.101216Z digest=sha256:c5a4b9ab7d02ed5aef1d531dd3318710610c996e29fb689b2603b50fc0b8902a

Observation 05118520-343d-41f6-a985-964db3dc6a91 · outbound

This paper cites Collective classification in network data.

Resolving Oversmoothing with Opinion Dissensus Collective classification in network data

Reference 86

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source=pdf_text observed=2026-08-09T21:29:28.106020Z digest=sha256:3145548915e067c3b02adbc0f2a7832fd8f73bffb21c887699868ef2859b9397

Observation 8aa65370-8800-4c2c-af04-50dc3a2fa23b · outbound

This paper cites Query-driven active surveying for collective classification.

Resolving Oversmoothing with Opinion Dissensus Query-driven active surveying for collective classification

Reference 87

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raw_fallback, observed 2026-08-09T21:29:28.822499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:28.110271Z digest=sha256:3cd84d8d68294f8457198dd6b585091b9353a7fd159a4156eede12b6db9c0da7

Observation 02c662f4-2587-421f-add5-6843c8e9753a · outbound

This paper cites Pitfalls of Graph Neural Network Evaluation.

Resolving Oversmoothing with Opinion Dissensus Pitfalls of Graph Neural Network Evaluation

Reference 88

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source=pdf_text observed=2026-08-09T21:29:28.115138Z digest=sha256:753f3916d6b968b97c89efe873f69f1b588057db886f901590c183304d0aadea

Observation fc13a07c-e88a-4276-890c-73df866e3dce · outbound

This paper cites Learning to extract symbolic knowledge from the world wide web.AAAI/IAAI, 3(3.6):2, 1998.

Resolving Oversmoothing with Opinion Dissensus Learning to extract symbolic knowledge from the world wide web.AAAI/IAAI, 3(3.6):2, 1998

Reference 89

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raw_fallback, observed 2026-08-09T21:29:28.807892Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:28.119576Z digest=sha256:9e576ddba1d002c04e029fa036b43df627a4832170fd4c451f093d37b8a2440d

Observation 8020a674-b598-4423-969a-dfdb435430ad · outbound

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

Resolving Oversmoothing with Opinion Dissensus Open graph benchmark: Datasets for machine learning on graphs

Reference 90

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source=pdf_text observed=2026-08-09T21:29:28.124424Z digest=sha256:f100308eef5e72be7d160cff8c07a2ce3a4ad7e3d57ff68d13558f72ce232eca

Observation cedf3e25-f1c9-4ebe-8839-260c508a2deb · outbound

This paper cites Matrix differential calculus with applications in statistics and econometrics.

Resolving Oversmoothing with Opinion Dissensus Matrix differential calculus with applications in statistics and econometrics

Reference 91

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verified fuzzy
raw_fallback, observed 2026-08-09T21:29:28.783601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:28.128804Z digest=sha256:414a720fcfa7455c3a9af8ce590612d92b0a79f38d08bf5ab943a422df5dffbc

Observation 4c33e68c-bea4-4f7a-8719-d52d01b831f6 · outbound

This paper cites Systems of differential equations that are competitive or cooperative ii: Convergence almost everywhere.

Resolving Oversmoothing with Opinion Dissensus Systems of differential equations that are competitive or cooperative ii: Convergence almost everywhere

Reference 92

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T21:29:28.767276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:28.133621Z digest=sha256:f813ce0d1957098e8451ddc6375c9b325ae67094f16815cb0e0985c959a251f2

Observation 6ec29272-e4c2-4f88-b227-0ea824e59a7c · outbound

This paper cites Image-based recommendations on styles and substitutes.

Resolving Oversmoothing with Opinion Dissensus Image-based recommendations on styles and substitutes

Reference 93

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T21:29:28.137939Z digest=sha256:959e298e1c2f81ef0c2d38547b1c36bd4fb6576f2018dd3777a86e7833a1c4e5

Observation 39e45667-0539-4cb0-af99-8d7fcb6defa7 · outbound

This paper cites Diffusion-convolutional neural networks.

Resolving Oversmoothing with Opinion Dissensus Diffusion-convolutional neural networks

Reference 94

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verified fuzzy
raw_fallback, observed 2026-08-09T21:29:28.742623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-09T21:29:28.142982Z digest=sha256:8903ee783cb594939a5a26b6c4360eddcf393c25f6c6233cb6c6b1f93fbf382f

Observation c3782279-124e-4144-8e52-45cf2f0fc219 · outbound

This paper cites Tune: A Research Platform for Distributed Model Selection and Training.

Resolving Oversmoothing with Opinion Dissensus Tune: A Research Platform for Distributed Model Selection and Training

Reference 95

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T21:29:28.147348Z digest=sha256:f1f034754cd0738fc4d8a0888d55249098695c870ca16a64ffb869ffeb77fa49

Pith citing papers

Observation 682825bd-26c9-4f5a-b5ca-03dcfc05532b · inbound

Topology-Preserving Neural Operator Learning via Hodge Decomposition cites this paper.

Topology-Preserving Neural Operator Learning via Hodge Decomposition Resolving Oversmoothing with Opinion Dissensus

Reference 9

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arxiv_id, observed 2026-05-14T19:07:51.537533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-14T19:05:31.289091Z digest=sha256:89e346fc96d9c0616d559d740d2a3971da37b9e63b650ad8ed46d28173faf4f1

Observation 8270ba44-4c04-4e19-a3c6-6d2a1b302b44 · inbound

Topology-Preserving Neural Operator Learning via Hodge Decomposition cites this paper.

Topology-Preserving Neural Operator Learning via Hodge Decomposition Resolving Oversmoothing with Opinion Dissensus

Reference 47

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metadata mismatch
arxiv_id, observed 2026-06-30T21:45:05.590857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-06-30T21:43:50.425136Z digest=sha256:4999bdd332742ca4c68f4fb222c68fa37a03b2ce53ff8e74e12e325a605475cf