Paper Citation Record · LEDGER

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions

As of 22 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2411.13358.

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

pith.paper-citation-record.v1
2411.13358 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T16:37:41.038503Z

measured 37 of 37 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

37 of 37 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8ed88f36-ed1f-4cd0-9ac5-c7b9e111454c · outbound

This paper cites Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio

Reference 1

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Observation 85d351d4-c822-454a-b374-9604e3073ba7 · outbound

This paper cites Auto-encoding variational bayes, 2022.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Auto-encoding variational bayes, 2022

Reference 2

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This paper cites Weiss, Niru Maheswaranathan, and Surya Ganguli.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Weiss, Niru Maheswaranathan, and Surya Ganguli

Reference 3

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Observation a808a8ac-064b-4ab2-931c-91607cb28b64 · outbound

This paper cites Denoising diffusion probabilistic models, 2020.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Denoising diffusion probabilistic models, 2020

Reference 4

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This paper cites Sheikh, and Eero P.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Sheikh, and Eero P

Reference 5

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Observation 014be6ca-20e8-46a2-a53b-39e3f6df3bb6 · outbound

This paper cites Efros, Eli Shechtman, and Oliver Wang.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Efros, Eli Shechtman, and Oliver Wang

Reference 6

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Observation c57e967a-b370-49d6-87ce-cdee164816b5 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 7

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Observation 26fcd339-49bd-4bf6-a850-65b43dd17897 · outbound

This paper cites Permutation invariant graph generation via score-based generative modeling.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Permutation invariant graph generation via score-based generative modeling

Reference 8

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Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Unresolved cited work

Reference 9

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This paper cites Hamilton, David Kristjanson Duvenaud, Raquel Urtasun, and Richard S.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Hamilton, David Kristjanson Duvenaud, Raquel Urtasun, and Richard S

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:37:41.418868Z

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Observation bcaf8279-c618-4348-a0d8-7c0483256e76 · outbound

This paper cites Equivariant diffusion for molecule generation in 3 D.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Equivariant diffusion for molecule generation in 3 D

Reference 11

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This paper cites DiGress: Discrete Denoising diffusion for graph generation.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions DiGress: Discrete Denoising diffusion for graph generation

Reference 12

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Observation a7935c88-0dfa-4a65-80c1-17ed5c9cdd3f · outbound

This paper cites Sulla determinazione emp \'i rica di uma legge di distribuzione.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Sulla determinazione emp \'i rica di uma legge di distribuzione

Reference 13

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Observation c7b1c91b-db71-49f5-9912-2b1666141f8a · outbound

This paper cites Efficient Graph Generation with Graph Recurrent Attention Networks.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Efficient Graph Generation with Graph Recurrent Attention Networks

Reference 14

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This paper cites Spectre: Spectral conditioning helps to overcome the expressivity limits of one-shot graph generators, 2022.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Spectre: Spectral conditioning helps to overcome the expressivity limits of one-shot graph generators, 2022

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:37:41.383727Z

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Observation 85002e64-a72b-4cd8-93ce-4e3c730f9e97 · outbound

This paper cites Fr \'e chet chemnet distance: A metric for generative models for molecules in drug discovery.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Fr \'e chet chemnet distance: A metric for generative models for molecules in drug discovery

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:37:41.371615Z

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Observation 18f55697-570f-4a89-84e2-df91f10ed92b · outbound

This paper cites Fast neighborhood subgraph pairwise distance kernel.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Fast neighborhood subgraph pairwise distance kernel

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:37:41.358867Z

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Observation b5a0a89a-62fa-431d-ac2d-c1dc48533c42 · outbound

This paper cites Evaluation metrics for graph generative models: Problems, pitfalls, and practical solutions.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Evaluation metrics for graph generative models: Problems, pitfalls, and practical solutions

Reference 18

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Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Unresolved cited work

Reference 19

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Observation 16ac6c43-1335-46c4-9d4e-cc72f3c87d88 · outbound

This paper cites How Powerful are Graph Neural Networks?.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions How Powerful are Graph Neural Networks?

Reference 20

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Observation dcff367e-b980-4708-9c46-b671a5d41488 · outbound

This paper cites Bronstein, and Bastian Rieck.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Bronstein, and Bastian Rieck

Reference 21

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Observation ac926366-b754-4b8d-8846-fa9c60a9e0a6 · outbound

This paper cites A survey of cross-validation procedures for model selection.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions A survey of cross-validation procedures for model selection

Reference 22

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Observation 194e2e1e-ad23-43b0-a30a-88320dae21d1 · outbound

This paper cites A Brief Review of Domain Adaptation.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions A Brief Review of Domain Adaptation

Reference 23

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This paper cites Earnshaw, Imran S.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Earnshaw, Imran S

Reference 24

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This paper cites Evaluating robustness and uncertainty of graph models under structural distributional shifts, 2023.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Evaluating robustness and uncertainty of graph models under structural distributional shifts, 2023

Reference 25

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Observation 36740124-02bb-4c77-9f10-d94ae093c35f · outbound

This paper cites The empirical beta copula.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions The empirical beta copula

Reference 26

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Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Unresolved cited work

Reference 27

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Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Borgwardt, and Bernhard Scholkopf

Reference 28

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This paper cites On the evolution of random graphs.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions On the evolution of random graphs

Reference 29

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Observation 500f2909-3d21-4286-bc0d-26b491ba4b51 · outbound

This paper cites Score-based generative modeling of graphs via the system of stochastic differential equations.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Score-based generative modeling of graphs via the system of stochastic differential equations

Reference 30

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Observation d1fee3ef-922d-461f-bb65-9f5721e7565a · outbound

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Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Doerksen

Reference 31

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This paper cites \ GG \ - \ gan \ : A geometric graph generative adversarial network, 2021.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions \ GG \ - \ gan \ : A geometric graph generative adversarial network, 2021

Reference 32

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Observation dd4dadc6-8648-4517-9829-cbf3991c3730 · outbound

This paper cites Digital Nets and Sequences: Discrepancy Theory and Quasi--Monte Carlo Integration.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Digital Nets and Sequences: Discrepancy Theory and Quasi--Monte Carlo Integration

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:37:41.189214Z

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Observation c7f9c06a-7478-4044-9df1-cef1a2cc809c · outbound

This paper cites Analysis of Kernel Mean Matching under Covariate Shift.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Analysis of Kernel Mean Matching under Covariate Shift

Reference 34

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This paper cites ADAPT : Awesome Domain Adaptation Python Toolbox.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions ADAPT : Awesome Domain Adaptation Python Toolbox

Reference 35

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Observation 78915de7-c00d-4ff0-a1e2-a932aac2da30 · outbound

This paper cites The tight constant in the dvoretzky-kiefer-wolfowitz inequality.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions The tight constant in the dvoretzky-kiefer-wolfowitz inequality

Reference 36

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Observation f7926884-41df-4e7b-8b65-a9dccab02f33 · outbound

This paper cites On the evolution of random graphs.

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions On the evolution of random graphs

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:37:41.168199Z

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

Pith citing papers

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