Pith. sign in

Paper Citation Record · LEDGER

Personalized Layer Selection for Graph Neural Networks

As of 13 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2501.14964.

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

pith.paper-citation-record.v1
2501.14964 v2

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:51:26.533489Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

18 of 18 outbound references displayed

  • verified exact1
  • verified fuzzy3
  • unresolved14
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 146548e5-b2ac-429a-9d3c-11a6cdfb1154 · outbound

This paper cites Sub-center arcface: Boosting face recognition by large-scale noisy web faces.

Personalized Layer Selection for Graph Neural Networks Sub-center arcface: Boosting face recognition by large-scale noisy web faces

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:51:26.826870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T14:51:26.459778Z digest=sha256:b22e2b89ba7b43b747670c82f35b8a6cbf9cfa1d77ea18e692913ce85d67f7e6

Observation 65033cf3-5946-4404-849c-2764c8630eac · outbound

This paper cites Geom-GCN: Geometric Graph Convolutional Networks.

Personalized Layer Selection for Graph Neural Networks Geom-GCN: Geometric Graph Convolutional Networks

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-10T14:51:26.483149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:51:26.483149Z digest=sha256:69a1d3b37cb2bae5472fa0f450605c340a24e25b4888f0618f11e73f9bf8545a

Observation 9301c884-8085-4cc3-a2c6-02fdadb4583b · outbound

This paper cites Metric Learning with Adaptive Density Discrimination.

Personalized Layer Selection for Graph Neural Networks Metric Learning with Adaptive Density Discrimination

Reference 9

Resolution
verified exact
local_arxiv, observed 2026-08-10T14:51:26.692934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T14:51:26.488056Z digest=sha256:012fb2f6043dd85a1478ddeabdce29d599ef78b5106c46c8365afba266d9e738

Observation c6b25a1e-a366-4d76-8b94-5003808473f5 · outbound

This paper cites DropEdge: Towards Deep Graph Convolutional Networks on Node Classification.

Personalized Layer Selection for Graph Neural Networks DropEdge: Towards Deep Graph Convolutional Networks on Node Classification

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-10T14:51:26.493034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:51:26.493034Z digest=sha256:3bca038bc69befc9b88f11582d26157f601342f247da13099040e32133d46ae3

Observation b0712cec-502a-4126-9ede-d974167009f7 · outbound

This paper cites Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification.

Personalized Layer Selection for Graph Neural Networks Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-10T14:51:26.508622Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:51:26.508622Z digest=sha256:d62c293d20cd8dc784da331381cd30b52838b098617f84d278270737f406ddb9

Observation 29e69bb1-3379-4525-a437-26730ca72b7a · outbound

This paper cites Non-target-specific node injection attacks on graph neural networks: A hierarchical reinforcement learning approach.

Personalized Layer Selection for Graph Neural Networks Non-target-specific node injection attacks on graph neural networks: A hierarchical reinforcement learning approach

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:51:26.797748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T14:51:26.513466Z digest=sha256:fdcbeddc9b83c5d9e97681b64a6d7714b947e45ad62533dd3d62982a97bf9838

Observation e2f19d02-acf9-4593-8627-cff015e074d9 · outbound

This paper cites Graph Attention Networks.

Personalized Layer Selection for Graph Neural Networks Graph Attention Networks

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-10T14:51:26.518224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:51:26.518224Z digest=sha256:83125c44ea8fb79ff7c83c28a81aa4d191ea11ceacdd35c5ab6667e4b56cf600

Observation e1ddd136-80cf-434e-ae0a-7377e1049938 · outbound

This paper cites Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective.

Personalized Layer Selection for Graph Neural Networks Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-10T14:51:26.523577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:51:26.523577Z digest=sha256:a67e022c13600fbeab746e64baf19eb859faf02114f72d29f22f0fda19a1bbda

Observation f9d73f23-a005-4614-914f-705392cae335 · outbound

This paper cites How Powerful are Graph Neural Networks?.

Personalized Layer Selection for Graph Neural Networks How Powerful are Graph Neural Networks?

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-10T14:51:26.528420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:51:26.528420Z digest=sha256:4026fb74291dfd39506dd5b57289710788670e400936bd475fce8dec28831d10

Observation 33c2c418-0031-4687-bd22-5296812b208c · outbound

This paper cites PairNorm: Tackling Oversmoothing in GNNs.

Personalized Layer Selection for Graph Neural Networks PairNorm: Tackling Oversmoothing in GNNs

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-10T14:51:26.533489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:51:26.533489Z digest=sha256:83f5c4315f07a9679f0c471e08423acb8c346d0e7aa40f6b091756017fb1fee1

Observation b8a33f61-8615-445f-a8c9-0dcf4504d151 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Personalized Layer Selection for Graph Neural Networks Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 2007

Resolution
unresolved
no resolver link, observed 2026-08-10T14:51:26.503340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:51:26.503340Z digest=sha256:d484698516243f9f849c9988e09e505ad39436c8b073eb8458de6e183616fcd4

Observation 3a0d56fa-eb6f-44ef-acce-3cef27faa17c · outbound

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

Personalized Layer Selection for Graph Neural Networks Semi-Supervised Classification with Graph Convolutional Networks

Reference 2015

Resolution
unresolved
no resolver link, observed 2026-08-10T14:51:26.473403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:51:26.473403Z digest=sha256:86fecc66ed16333d6d54198ff14b3a30cbed336024b164dd57dd228feeb90c23

Observation e256e2fb-5980-41e2-be9d-a0e9c881b678 · outbound

This paper cites How Attentive are Graph Attention Networks?.

Personalized Layer Selection for Graph Neural Networks How Attentive are Graph Attention Networks?

Reference 2017

Resolution
unresolved
no resolver link, observed 2026-08-10T14:51:26.449687Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:51:26.449687Z digest=sha256:2f40c6fda2256b16da0fc4186513dc442a95e511eb4e9b330f6091ff99004392

Observation 45228002-d333-425b-bab6-ef6be268cc21 · outbound

This paper cites Adaptive Universal Generalized PageRank Graph Neural Network.

Personalized Layer Selection for Graph Neural Networks Adaptive Universal Generalized PageRank Graph Neural Network

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-10T14:51:26.455103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:51:26.455103Z digest=sha256:75266a618bcb9590cdff57ed599c9d6974f25b84f85ebe6fc04216e978d26bf1

Observation d48ef345-abcf-4700-8a07-1705e5eb7da3 · outbound

This paper cites Understanding convolution on graphs via energies.

Personalized Layer Selection for Graph Neural Networks Understanding convolution on graphs via energies

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-10T14:51:26.464405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:51:26.464405Z digest=sha256:94fd5763a09fea356432c6d57be0b4f34632c8562aaf157265ad8d94e61f4103

Observation d0fb193a-e20c-41d3-9fd9-46fff6f0bedb · outbound

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

Personalized Layer Selection for Graph Neural Networks Revisiting Graph Neural Networks: All We Have is Low-Pass Filters

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-10T14:51:26.478201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:51:26.478201Z digest=sha256:bb759822d2b275be4d67c9dc6555e0d514249c11cd742d82299a8c4a98ee7b24

Observation 15173fff-907d-40c9-b4be-2fb821deed0b · outbound

This paper cites Drew: Dynamically rewired message passing with delay.

Personalized Layer Selection for Graph Neural Networks Drew: Dynamically rewired message passing with delay

Reference 2023

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:51:26.812117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T14:51:26.468990Z digest=sha256:114ac36f13cbb9a20d0736cd5de37791a64f7269691ce684b942ac3f08da3a65

Observation 72faa079-8f2f-4976-a848-abb6b9f09896 · outbound

This paper cites Gradient Gating for Deep Multi-Rate Learning on Graphs.

Personalized Layer Selection for Graph Neural Networks Gradient Gating for Deep Multi-Rate Learning on Graphs

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-10T14:51:26.498276Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:51:26.498276Z digest=sha256:6cee54c09a33f999aa304ad4555309e213bb15f43a46ddac87543e1277bb4339

Pith citing papers

No inbound Pith citation observations are available.