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Paper Citation Record · LEDGER

Residual Gated Graph ConvNets

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 57 inbound Pith citation observations for arXiv:1711.07553.

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

pith.paper-citation-record.v1
1711.07553 v2

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measured 0 of 0 reference resolution

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measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 57 of 57 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:12:35.071633Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

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0 of 0 outbound references displayed

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External citation measurements

249
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 65d406a0-ac8f-41d8-9e60-10ce1bcfcb9f · inbound

Hugging Rain Man: A Novel Facial Action Units Dataset for Analyzing Atypical Facial Expressions in Children with Autism Spectrum Disorder cites this paper.

Hugging Rain Man: A Novel Facial Action Units Dataset for Analyzing Atypical Facial Expressions in Children with Autism Spectrum Disorder Residual Gated Graph ConvNets

Reference 80

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Observation 3084986c-5095-49c3-a10c-b3f5cf1004d6 · inbound

GrokFormer: Graph Fourier Kolmogorov-Arnold Transformers cites this paper.

GrokFormer: Graph Fourier Kolmogorov-Arnold Transformers Residual Gated Graph ConvNets

Reference 2005

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Observation 3cd7cccc-6c45-423e-8e52-99b1013419a4 · inbound

ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment cites this paper.

ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment Residual Gated Graph ConvNets

Reference 2024

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Observation 1a6ef0f2-1955-4fc6-a5ea-ff518b53c6d2 · inbound

Node Classification With Integrated Reject Option cites this paper.

Node Classification With Integrated Reject Option Residual Gated Graph ConvNets

Reference 3

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source=arxiv_source observed=2026-08-11T22:46:26.285714Z digest=sha256:fafcafae1d97c3a4754c89a37ff2c4655681e8f3f68e98f6e391951fd91c5293

Observation a81f525b-2b9b-4802-ac81-12f4880f7b04 · inbound

RingFormer: A Ring-Enhanced Graph Transformer for Organic Solar Cell Property Prediction cites this paper.

RingFormer: A Ring-Enhanced Graph Transformer for Organic Solar Cell Property Prediction Residual Gated Graph ConvNets

Reference 4

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Observation 3f586a21-525a-4165-ab13-94238ab43722 · inbound

GraSP: Simple yet Effective Graph Similarity Predictions cites this paper.

GraSP: Simple yet Effective Graph Similarity Predictions Residual Gated Graph ConvNets

Reference 5

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Observation afe06e31-0cf0-4566-8f9a-56cff09a9548 · inbound

Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs cites this paper.

Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs Residual Gated Graph ConvNets

Reference 2017

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Observation f6b6f4e1-7380-45e0-ab57-4d0a04533f2e · inbound

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality cites this paper.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Residual Gated Graph ConvNets

Reference 14

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source=arxiv_source observed=2026-08-10T22:02:05.035190Z digest=sha256:7b93f72d1d98c41367f01bf04f70e7cebfe58e550260399827f354cd0a54baae

Observation 71b5dbcc-959d-4c8a-8141-a750bf5efeed · inbound

GRAMA: Adaptive Graph Autoregressive Moving Average Models cites this paper.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Residual Gated Graph ConvNets

Reference 15

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source=arxiv_source observed=2026-08-10T16:57:29.042022Z digest=sha256:639a50c83f3a128be19e800aa8d0f42c80c3902ee5fecf2811933a234c71398a

Observation 80f96444-2932-4abc-a29f-d3ae82fbc563 · inbound

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture cites this paper.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Residual Gated Graph ConvNets

Reference 5

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source=arxiv_source observed=2026-08-09T19:38:34.620516Z digest=sha256:215560db387db8abb946108943b09b671e0a83af250976b7e9daaa1f356e7794

Observation 6ca46ad6-5d4f-4ccb-9eea-d9b96171cc29 · inbound

Simple Path Structural Encoding for Graph Transformers cites this paper.

Simple Path Structural Encoding for Graph Transformers Residual Gated Graph ConvNets

Reference 7

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source=arxiv_source observed=2026-08-07T21:50:23.392461Z digest=sha256:cbc22551b3d98013dc3d33e3a9ed88dd9b0658aad7e933fb64c3e8408a5aead3

Observation 3aca7c53-e87d-4228-8850-27fac98ef3ea · inbound

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements cites this paper.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Residual Gated Graph ConvNets

Reference 65

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Observation ab2be898-f239-43c9-8374-37f65f9fd13d · inbound

Kinship Verification through a Forest Neural Network cites this paper.

Kinship Verification through a Forest Neural Network Residual Gated Graph ConvNets

Reference 48

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Observation e8c6956a-7023-43f5-a222-249e0a54c8af · inbound

SFi-Former: Sparse Flow Induced Attention for Graph Transformer cites this paper.

SFi-Former: Sparse Flow Induced Attention for Graph Transformer Residual Gated Graph ConvNets

Reference 7

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Observation 5eaf136b-f397-420e-98bc-fbc18a43e845 · inbound

Learning traffic flows: Graph Neural Networks for Metamodelling Traffic Assignment cites this paper.

Learning traffic flows: Graph Neural Networks for Metamodelling Traffic Assignment Residual Gated Graph ConvNets

Reference 35

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Observation f68bae8c-da71-4e9d-9da4-e9dee780334b · inbound

SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation cites this paper.

SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Residual Gated Graph ConvNets

Reference 31

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Observation 4a432ef6-f29b-4214-bde4-b67400965fba · inbound

Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling cites this paper.

Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling Residual Gated Graph ConvNets

Reference 13

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Observation 0cb38f1a-626a-4f10-b0a5-b71934ebca52 · inbound

Improving the Effective Receptive Field of Message-Passing Neural Networks cites this paper.

Improving the Effective Receptive Field of Message-Passing Neural Networks Residual Gated Graph ConvNets

Reference 8

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Observation 618cd6b7-3f21-4770-9678-e43fb9700829 · inbound

Towards Efficient Few-shot Graph Neural Architecture Search via Partitioning Gradient Contribution cites this paper.

Towards Efficient Few-shot Graph Neural Architecture Search via Partitioning Gradient Contribution Residual Gated Graph ConvNets

Reference 4

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Observation c333df4b-56dc-4c35-9a69-a52e2298d3a1 · inbound

On Measuring Long-Range Interactions in Graph Neural Networks cites this paper.

On Measuring Long-Range Interactions in Graph Neural Networks Residual Gated Graph ConvNets

Reference 2021

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Observation 91bebf9f-7e67-40e2-b7c2-ea28c35230a2 · inbound

EFormer: An Effective Edge-based Transformer for Vehicle Routing Problems cites this paper.

EFormer: An Effective Edge-based Transformer for Vehicle Routing Problems Residual Gated Graph ConvNets

Reference 5

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Observation aacc6892-2845-49af-ad96-e9aa4717a3a2 · inbound

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction cites this paper.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction Residual Gated Graph ConvNets

Reference 25

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Observation 4e89d3d5-aaf8-4264-ad64-79166fed9d9b · inbound

Robust Anomaly Detection with Graph Neural Networks using Controllability cites this paper.

Robust Anomaly Detection with Graph Neural Networks using Controllability Residual Gated Graph ConvNets

Reference 11

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Observation 4d567ec1-df01-48d8-9ccc-4da735b3ae45 · inbound

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning cites this paper.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Residual Gated Graph ConvNets

Reference 22

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Observation f4b577b2-6d0d-474e-a981-f5a929137ae8 · inbound

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows cites this paper.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Residual Gated Graph ConvNets

Reference 12

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Observation 0e823930-874f-4795-94bc-ce5b1813fcf9 · inbound

A Spatio-Temporal Graph Neural Networks Approach for Predicting Silent Data Corruption inducing Circuit-Level Faults cites this paper.

A Spatio-Temporal Graph Neural Networks Approach for Predicting Silent Data Corruption inducing Circuit-Level Faults Residual Gated Graph ConvNets

Reference 40

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Observation ef8354b3-e2bb-42b6-a2c4-d123547a87cd · inbound

Adaptive Canonicalization with Application to Invariant Anisotropic Geometric Networks cites this paper.

Adaptive Canonicalization with Application to Invariant Anisotropic Geometric Networks Residual Gated Graph ConvNets

Reference 2

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local_arxiv, observed 2026-05-18T12:46:23.286508Z

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Observation ed0407ea-bd77-4956-8240-1afee79ebc7d · inbound

Learning from Historical Activations in Graph Neural Networks cites this paper.

Learning from Historical Activations in Graph Neural Networks Residual Gated Graph ConvNets

Reference 3

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local_arxiv, observed 2026-05-21T17:15:25.390984Z

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Observation 5474bdfe-e307-4f83-89e7-a07be42c4768 · inbound

Extracting Money Laundering Transactions from Quasi-Temporal Graph Representation cites this paper.

Extracting Money Laundering Transactions from Quasi-Temporal Graph Representation Residual Gated Graph ConvNets

Reference 6

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arxiv_id, observed 2026-05-13T20:53:15.015915Z

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Observation 2f9d3d1d-b942-4d9b-8b80-3ec640099ddf · inbound

BiScale-GTR: Fragment-Aware Graph Transformers for Multi-Scale Molecular Representation Learning cites this paper.

BiScale-GTR: Fragment-Aware Graph Transformers for Multi-Scale Molecular Representation Learning Residual Gated Graph ConvNets

Reference 1

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arxiv_id, observed 2026-05-10T23:50:54.370601Z

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Observation 7b386171-588f-4118-9e99-08b1bc3290eb · inbound

R2G: A Multi-View Circuit Graph Benchmark Suite from RTL to GDSII cites this paper.

R2G: A Multi-View Circuit Graph Benchmark Suite from RTL to GDSII Residual Gated Graph ConvNets

Reference 4

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arxiv_id, observed 2026-05-11T07:41:01.270285Z

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Observation e1514368-dcc3-4583-84e0-02dffcc8a740 · inbound

Neighbourhood Transformer: Switchable Attention for Monophily-Aware Graph Learning cites this paper.

Neighbourhood Transformer: Switchable Attention for Monophily-Aware Graph Learning Residual Gated Graph ConvNets

Reference 4

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arxiv_id, observed 2026-05-11T05:40:58.470538Z

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Observation 42e2348b-c340-4446-9372-c299a34d1af4 · inbound

Capacity-Controlled Global Attention for Graph Transformers cites this paper.

Capacity-Controlled Global Attention for Graph Transformers Residual Gated Graph ConvNets

Reference 17

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arxiv_id, observed 2026-05-10T06:31:30.764782Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-10T06:27:24.557866Z digest=sha256:8d8cdcb7ce419f2be6531b64ba13b5614c5a2f16ee71af0472327f90f7273d0e

Observation d245fff9-ebdf-400f-b381-acc8e539cc1f · inbound

Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors cites this paper.

Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors Residual Gated Graph ConvNets

Reference 86

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arxiv_id, observed 2026-05-11T12:21:04.875605Z

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source=arxiv_source observed=2026-05-10T03:50:44.626261Z digest=sha256:9145e1378973f37f4fff8494616fe2e0d750bc00c1730d297d47312951566af9

Observation 6b57f4e3-e3bc-4ae2-ae97-a9057fe56c49 · inbound

On the Expressive Power of GNNs to Solve Linear SDPs cites this paper.

On the Expressive Power of GNNs to Solve Linear SDPs Residual Gated Graph ConvNets

Reference 3

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 7c196366-cc2c-4d51-8390-a8141545c005 · inbound

A Multi-View Media Profiling Suite: Resources, Evaluation, and Analysis cites this paper.

A Multi-View Media Profiling Suite: Resources, Evaluation, and Analysis Residual Gated Graph ConvNets

Reference 13

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arxiv_id, observed 2026-05-11T16:41:15.333337Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-05-09T15:22:45.263415Z digest=sha256:ea8e50df91a7a114aa069e56b52c6b383b2c821d7298d60e7183e81895328a28

Observation d21c0073-b736-4a42-a7ae-92ac7b6abe45 · inbound

GCCM: Enhancing Generative Graph Prediction via Contrastive Consistency Model cites this paper.

GCCM: Enhancing Generative Graph Prediction via Contrastive Consistency Model Residual Gated Graph ConvNets

Reference 25

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arxiv_id, observed 2026-05-11T19:31:08.963860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-08T11:46:42.010486Z digest=sha256:8e73391ce1db3f2400ec555ce23c0b2e1f51c70aee386b09f75a209abba02157

Observation cd60123c-e9a8-45c5-8615-8cf89448bdf2 · inbound

No Triangulation Without Representation: Generalization in Topological Deep Learning cites this paper.

No Triangulation Without Representation: Generalization in Topological Deep Learning Residual Gated Graph ConvNets

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-11T19:06:11.105732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-08T12:37:30.458352Z digest=sha256:810af36fe70ecc3f241571c65db1c964eae0b2a6fe18bacd4af0011282dce20f

Observation d8c27550-bedd-4b5d-8f25-c3a83aba0902 · inbound

Towards Metric-Faithful Neural Graph Matching cites this paper.

Towards Metric-Faithful Neural Graph Matching Residual Gated Graph ConvNets

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-05-11T19:21:07.195557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-08T12:16:49.816847Z digest=sha256:602366c628e02cfc555f67cba48a595d815b9cbb86f12370cf67b08ab78e0888

Observation 66ae9d40-eccc-482a-80b3-8491625af365 · inbound

Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks cites this paper.

Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks Residual Gated Graph ConvNets

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-11T05:00:55.663513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-11T00:55:32.471978Z digest=sha256:8d9582447fac8c2a6a65c7094033262e7fdb53405d5374e9746c2de9b9695853

Observation f8de969f-4f9f-4e42-88b1-f8e17dcc082d · inbound

Why Self-Inconsistency Arises in GNN Explanations and How to Exploit It cites this paper.

Why Self-Inconsistency Arises in GNN Explanations and How to Exploit It Residual Gated Graph ConvNets

Reference 42

Resolution
malformed identifier
arxiv_id, observed 2026-05-11T03:50:55.248974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-11T02:14:02.401190Z digest=sha256:9ae8f1b46df47526486a427650cb0bf9b38c2cd8c2b3013a7e030dbe9439ba4f

Observation a0edb909-d447-4394-9e03-07dcd4347ec0 · inbound

Why Self-Inconsistency Arises in GNN Explanations and How to Exploit It cites this paper.

Why Self-Inconsistency Arises in GNN Explanations and How to Exploit It Residual Gated Graph ConvNets

Reference 42

Resolution
malformed identifier
local_arxiv, observed 2026-06-30T23:15:07.890292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-06-30T23:14:31.835825Z digest=sha256:ae2619234693c69805adb330fc72dafcffdfda8e61250316ec3d66951cf36a68

Observation 16220316-c165-49a9-9904-f259fa84b8bb · inbound

COAgents: Multi-Agent Framework to Learn and Navigate Routing Problems Search Space cites this paper.

COAgents: Multi-Agent Framework to Learn and Navigate Routing Problems Search Space Residual Gated Graph ConvNets

Reference 1

Resolution
metadata mismatch
local_arxiv, observed 2026-05-21T05:29:39.182434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-21T05:25:44.647620Z digest=sha256:b4ce57720ae7899b5990f85f4f7840936f78dc74494b5fbc44683d5bab54039d

Observation 96bfa441-97e4-426a-85ca-0747e94f5be9 · inbound

Weisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them cites this paper.

Weisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them Residual Gated Graph ConvNets

Reference 50

Resolution
verified exact
local_arxiv, observed 2026-05-25T04:46:37.339431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-25T04:45:41.043520Z digest=sha256:a03b28afaa757ce20323f23308a43928c8538ed53412d66ec1e8b1868bb0edde

Observation a673b508-0a38-41d2-8cb3-cda684bbce78 · inbound

Learning Dynamic Stability Landscapes in Synchronization Networks cites this paper.

Learning Dynamic Stability Landscapes in Synchronization Networks Residual Gated Graph ConvNets

Reference 82

Resolution
verified exact
local_arxiv, observed 2026-05-25T05:05:20.779419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-05-25T05:04:16.957305Z digest=sha256:4139125ddba6029ed49b68b738b681c66fa67ff6af0405c2b541491ef7b6dcd0

Observation 230120bb-109d-42d0-a442-986f0175cec1 · inbound

Understanding and Reducing Metadata-Driven Host Overheads in Sampling-Based GNN Training cites this paper.

Understanding and Reducing Metadata-Driven Host Overheads in Sampling-Based GNN Training Residual Gated Graph ConvNets

Reference 1

Resolution
verified exact
local_arxiv, observed 2026-06-29T06:03:08.464226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-06-29T05:59:41.958293Z digest=sha256:77064854507a462d87b38bf10a86afd2315c80d0217d844fff49cccb8ab75ed1

Observation f8545d53-a471-433b-9f57-b124c2399abd · inbound

Learn When and Where to Connect: Adaptive Virtual Nodes for Dynamic Message Passing on Graphs cites this paper.

Learn When and Where to Connect: Adaptive Virtual Nodes for Dynamic Message Passing on Graphs Residual Gated Graph ConvNets

Reference 8

Resolution
verified exact
local_arxiv, observed 2026-06-28T11:22:03.000965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-06-28T11:14:14.178933Z digest=sha256:f3ef640457b068b8d592bdfa24936dbdfc9e0f1a3897ba35a555e460d75a8fab

Observation 01663338-d8b0-430d-8ae2-0ffa816fc1d5 · inbound

Contrastive Neural Algorithmic Reasoning for Graph Coloring cites this paper.

Contrastive Neural Algorithmic Reasoning for Graph Coloring Residual Gated Graph ConvNets

Reference 4

Resolution
verified exact
local_arxiv, observed 2026-07-02T02:06:26.453225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-06-28T11:18:49.300810Z digest=sha256:7a131c74c95efb40688c15eec925e9f37525bbd586848389460687eb64da0cd8

Observation cbeeebc8-9599-4af6-ab1d-d3b9f8f391de · inbound

ALINC: Active Learning for Inductive Node Classification via Graph Sampling cites this paper.

ALINC: Active Learning for Inductive Node Classification via Graph Sampling Residual Gated Graph ConvNets

Reference 4

Resolution
metadata mismatch
local_arxiv, observed 2026-07-02T07:06:44.357860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-06-28T07:08:45.831294Z digest=sha256:c7e6c60f33c513d7a85e217862efee73e3c150630533910bf0f6699b0d828667

Observation f62bf90e-7cba-495f-9b9a-7efaef867e08 · inbound

End-to-End Subgraph Detection with GraphDETR cites this paper.

End-to-End Subgraph Detection with GraphDETR Residual Gated Graph ConvNets

Reference 3

Resolution
verified exact
local_arxiv, observed 2026-07-02T11:46:56.098257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-06-28T02:54:47.401548Z digest=sha256:0c3014f679f17ccfe2e40cea36256b09bede25c82c7ff16848626b36640dd90e

Observation daafe01e-a68a-449b-8d03-9569477114c6 · inbound

Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation cites this paper.

Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation Residual Gated Graph ConvNets

Reference 152

Resolution
verified exact
local_arxiv, observed 2026-06-26T11:09:23.769815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-06-26T10:59:25.867813Z digest=sha256:0a3f21149aa64ba896b28bde1a89c45996716e44480bf05a66c76cdf17f2dc56

Observation 09d22e79-f9ad-45c7-a71b-470e990267a0 · inbound

X-LogSMask: Expand Transformer for Graph-Structured Data cites this paper.

X-LogSMask: Expand Transformer for Graph-Structured Data Residual Gated Graph ConvNets

Reference 34

Resolution
verified exact
local_arxiv, observed 2026-07-03T01:07:29.725857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-07-03T01:00:18.263578Z digest=sha256:936bb731631dded631184f4b3b7c342830ab259eeac71f4dde2d6b4d17ca36e9

Observation 38ca0a40-ff68-44fb-9042-5a41771bd2b5 · inbound

Benign Overfitting Does Not Occur in Diffusion Models cites this paper.

Benign Overfitting Does Not Occur in Diffusion Models Residual Gated Graph ConvNets

Reference 139

Resolution
unresolved
no resolver link, observed 2026-07-12T07:49:39.894643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T07:49:39.894643Z digest=sha256:b09a6e5cd909206a6b07c8b1e250a81d38be4c508ba093b930bf4554365637e7

Observation e2675178-34b0-4dc9-9c7e-8633a54d89db · inbound

MxGPS: Multiplex Graph Transformers for a Power Grid Foundation Model cites this paper.

MxGPS: Multiplex Graph Transformers for a Power Grid Foundation Model Residual Gated Graph ConvNets

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-02T03:50:44.362087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T03:50:44.362087Z digest=sha256:4a50be18a0bbc191d39502591ed558b2cca5bac191a04192d12c2c166716632c

Observation ab4f379b-f3fc-4565-a690-c8dcc25dca81 · inbound

GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models cites this paper.

GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models Residual Gated Graph ConvNets

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-01T12:56:38.531562Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T12:56:38.531562Z digest=sha256:b3416e14e38dbe2e5dce5bed7aa51ac1e07bf9dbadc2752fcdfb1f7d3d4b385a

Observation d9ae5dce-d2dc-4009-b03d-24e46a0577d1 · inbound

CondPSE: A Polynomial-Filtered Structural Encoder with Conditional Modulation for Graphs cites this paper.

CondPSE: A Polynomial-Filtered Structural Encoder with Conditional Modulation for Graphs Residual Gated Graph ConvNets

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-01T03:16:39.641135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T03:16:39.641135Z digest=sha256:d76f0c98a1b85713a80aabfd441e19d7cbc53a07d3497f402199103b683aabb7

Observation ec74cf3f-dd88-45c9-af58-a8ea1a05dcf7 · inbound

SILVA Networks as Structured Implicit Layers and Vector Attractors via Dynamic Interaction Fields cites this paper.

SILVA Networks as Structured Implicit Layers and Vector Attractors via Dynamic Interaction Fields Residual Gated Graph ConvNets

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-03T15:56:12.953752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T15:56:12.953752Z digest=sha256:2f01ec48fa8f0f049cf2cd22de6544e16bdceb8472c06b039f211093331e10a3