Paper Citation Record · LEDGER

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models

As of 21 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:1908.05081.

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

pith.paper-citation-record.v1
1908.05081 v3

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:30:43.778897Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

21 of 21 outbound references displayed

verified exact0
verified fuzzy15
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 23abfece-dc55-4ebb-bc68-afa050642643 · outbound

This paper cites N-gcn: Multi-scale graph con- volution for semi-supervised node classiﬁcation.International Workshop on Mining and Learning with Graphs (MLG), 2018a.

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models N-gcn: Multi-scale graph con- volution for semi-supervised node classiﬁcation.International Workshop on Mining and Learning with Graphs (MLG), 2018a

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:44.042911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e8848de3-5f43-4b32-af10-09a48f7b3434 · outbound

This paper cites Spectral networks and locally connected networks on graphs.

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models Spectral networks and locally connected networks on graphs

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:44.019856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fe48490d-c823-4d9c-b4b3-8ea0b4cf01c0 · outbound

This paper cites A new model for learning in graph domains.

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models A new model for learning in graph domains

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:43.982403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 337cb0c6-9cc8-42c3-b82f-9b929826705e · outbound

This paper cites Lanczosnet: Multi-scale deep graph convolutional networks.

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models Lanczosnet: Multi-scale deep graph convolutional networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:43.943492Z

Source-reported events for the cited work

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Observation a0c7ad3a-276a-47aa-9f7e-854667d2d4ef · outbound

This paper cites Graph attention networks.

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models Graph attention networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:43.922212Z

Source-reported events for the cited work

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Observation 3d47d944-dce4-42ef-ae03-7f26b8133602 · outbound

This paper cites Simplifying graph convolutional networks.

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models Simplifying graph convolutional networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:43.911667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 35c45b93-4831-4bef-b5e2-4ebcd219ce3b · outbound

This paper cites How powerful are graph neural networks? ICLR, 2018a.

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models How powerful are graph neural networks? ICLR, 2018a

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:43.900507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9536ceed-ab54-47c4-8471-21dcf22c2e72 · outbound

This paper cites an unresolved cited work.

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-14T13:30:43.887491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 159d5ed0-5203-4b88-8524-03ce3cc7cfdf · outbound

This paper cites an unresolved cited work.

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-14T13:30:43.875213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c2e7c15b-abac-435d-9b8a-a5d4ec75ad9a · outbound

This paper cites an unresolved cited work.

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-14T13:30:43.863765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5ac81f90-826c-4917-b2e6-0f36238d0f96 · outbound

This paper cites It is strongly Bayes-risk consistent if limn→∞L(hn) = a almost surely.

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models It is strongly Bayes-risk consistent if limn→∞L(hn) = a almost surely

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:43.852092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e22fe503-5d07-4db6-ad3d-c6177f64d78e · outbound

This paper cites an unresolved cited work.

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-14T13:30:43.836856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d79ea73c-bfa5-47d4-ad72-0a91facdba17 · outbound

This paper cites Fastgcn: fast learning with graph convolutional networks via importance sampling.

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models Fastgcn: fast learning with graph convolutional networks via importance sampling

Reference 2003

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:43.996255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 90605009-f444-4e1d-b5d5-a6348ae645a0 · outbound

This paper cites Power up! Robust Graph Convolutional Network via Graph Powering.

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models Power up! Robust Graph Convolutional Network via Graph Powering

Reference 2004

Resolution: unresolved; no resolver link, observed 2026-08-14T13:30:43.721910Z

Source-reported events for the cited work

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Observation 9800963d-8469-4f54-ae6f-593f14e178dc · outbound

This paper cites Max-Margin Nonparametric Latent Feature Models for Link Prediction.

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models Max-Margin Nonparametric Latent Feature Models for Link Prediction

Reference 2005

Resolution: unresolved; no resolver link, observed 2026-08-14T13:30:43.760108Z

Source-reported events for the cited work

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Observation cc223570-a15e-40ca-8acd-a72401f51595 · outbound

This paper cites Pitfalls of graph neural network evaluation.In Relational Representation Learning Workshop (R2L 2018), NeurIPS,.

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models Pitfalls of graph neural network evaluation.In Relational Representation Learning Workshop (R2L 2018), NeurIPS,

Reference 2008

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:43.932735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation be7927d7-2b87-42f4-931a-cd905d369b73 · outbound

This paper cites Bootstrapped graph diffusions: Exposing the power of nonlinear- ity.

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models Bootstrapped graph diffusions: Exposing the power of nonlinear- ity

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:44.008657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation be126397-7ceb-4630-8df5-2a8cd9f9d304 · outbound

This paper cites Learning deep resnet blocks sequentially using boosting theory.

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models Learning deep resnet blocks sequentially using boosting theory

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:43.973279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f09fbfb4-c649-45b1-b44f-167e83804e49 · outbound

This paper cites Predict then propagate: Graph neural networks meet personalized pagerank.

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models Predict then propagate: Graph neural networks meet personalized pagerank

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:43.953995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 39dde871-8c05-46a6-9df8-be7fc12f15db · outbound

This paper cites Deep gaussian embedding of graphs: Unsuper- vised inductive learning via ranking.

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models Deep gaussian embedding of graphs: Unsuper- vised inductive learning via ranking

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:44.030972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f38635fe-03f0-493b-a59d-ed757dd8c5ab · outbound

This paper cites Semi-supervised classiﬁcation with graph convolutional net- works.

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models Semi-supervised classiﬁcation with graph convolutional net- works

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:43.963871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

No inbound Pith citation observations are available.