Paper Citation Record · LEDGER

Graph Generative Pre-trained Transformer

As of 23 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 5 inbound Pith citation observations for arXiv:2501.01073.

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

pith.paper-citation-record.v1
2501.01073 v2

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:40:48.890066Z

measured 56 of 56 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T05:19:22.557090Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T04:33:39.707945Z

Reference resolution

51 of 51 outbound references displayed

verified exact3
verified fuzzy9
unresolved37
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External citation measurements

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Outbound references

Observation 25b99da7-9f04-46fa-8f9f-9456730d3cf5 · outbound

This paper cites GPT-4 Technical Report.

Graph Generative Pre-trained Transformer GPT-4 Technical Report

Reference 1

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Observation 2aaf97d5-781c-4aa9-8bfa-af71a8f1d69e · outbound

This paper cites Among them, GRAN (Liao et al., 2019), BiGG (Dai et al., 2020), and BwR (Diamant et al.,.

Graph Generative Pre-trained Transformer Among them, GRAN (Liao et al., 2019), BiGG (Dai et al., 2020), and BwR (Diamant et al.,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:40:49.388932Z

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Observation fb8377de-b8f5-4c12-a075-71f074135b4d · outbound

This paper cites Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation.

Graph Generative Pre-trained Transformer Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation

Reference 6

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Observation 93f5b092-0c62-407f-b0a3-86903de4bbe8 · outbound

This paper cites MolGAN: An implicit generative model for small molecular graphs.

Graph Generative Pre-trained Transformer MolGAN: An implicit generative model for small molecular graphs

Reference 8

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Observation fe5d4c77-8f04-4f44-8136-9be5457de179 · outbound

This paper cites 2024; Xu et al., 2024; Wang et al.,.

Graph Generative Pre-trained Transformer 2024; Xu et al., 2024; Wang et al.,

Reference 9

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Observation c3b66e5c-e7fd-49c1-9334-1eb240b2cbfa · outbound

This paper cites The Llama 3 Herd of Models.

Graph Generative Pre-trained Transformer The Llama 3 Herd of Models

Reference 11

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Source-reported events for the cited work

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Observation 09ecd560-f9cc-4358-8800-978cedcfab5e · outbound

This paper cites A Generalization of Transformer Networks to Graphs.

Graph Generative Pre-trained Transformer A Generalization of Transformer Networks to Graphs

Reference 12

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Observation 1db7f8be-f44b-4c24-b8d6-0015c7bc6705 · outbound

This paper cites Variational Flow Matching for Graph Generation.

Graph Generative Pre-trained Transformer Variational Flow Matching for Graph Generation

Reference 13

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Observation 4533980d-781c-4596-b441-e27057a2362c · outbound

This paper cites A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer.

Graph Generative Pre-trained Transformer A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer

Reference 14

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Observation 61b63783-4eb6-4155-9549-dc8438549a51 · outbound

This paper cites Discrete Flow Matching.

Graph Generative Pre-trained Transformer Discrete Flow Matching

Reference 15

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Observation cd5057d6-dc73-476c-b1d3-00b5e75e7ab4 · outbound

This paper cites Diffusion Models for Graphs Benefit From Discrete State Spaces.

Graph Generative Pre-trained Transformer Diffusion Models for Graphs Benefit From Discrete State Spaces

Reference 16

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Observation 81b47bd7-9d2f-45f5-9461-8bb1934de629 · outbound

This paper cites GraphMAE: Self-Supervised Masked Graph Autoencoders.

Graph Generative Pre-trained Transformer GraphMAE: Self-Supervised Masked Graph Autoencoders

Reference 17

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Observation 3755a571-ad2a-40ec-a708-d3d4f204eee3 · outbound

This paper cites A Simple and Scalable Representation for Graph Generation.

Graph Generative Pre-trained Transformer A Simple and Scalable Representation for Graph Generation

Reference 18

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Observation 9d269ea7-d74f-433d-83ea-7f3c1ccb56c6 · outbound

This paper cites Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations.

Graph Generative Pre-trained Transformer Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations

Reference 19

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Observation 706279e6-4e0b-4898-bb97-b00b6d86e0ce · outbound

This paper cites Learning Deep Generative Models of Graphs.

Graph Generative Pre-trained Transformer Learning Deep Generative Models of Graphs

Reference 20

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Observation 3eb34eae-54b7-4228-b0e4-15978832622c · outbound

This paper cites Flow Matching for Generative Modeling.

Graph Generative Pre-trained Transformer Flow Matching for Generative Modeling

Reference 21

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Observation 8f9c6fd4-8105-4b9a-aadd-6a5a19d6e4b4 · outbound

This paper cites Pre-training Molecular Graph Representation with 3D Geometry.

Graph Generative Pre-trained Transformer Pre-training Molecular Graph Representation with 3D Geometry

Reference 22

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Observation a11d659e-d222-4924-8468-53926bf8cd5c · outbound

This paper cites Decoupled Weight Decay Regularization.

Graph Generative Pre-trained Transformer Decoupled Weight Decay Regularization

Reference 23

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Observation 53740c6a-169b-4d56-9b00-dcb9c977d99d · outbound

This paper cites GraphNVP: An Invertible Flow Model for Generating Molecular Graphs.

Graph Generative Pre-trained Transformer GraphNVP: An Invertible Flow Model for Generating Molecular Graphs

Reference 24

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Observation 287f9687-485f-4e53-ab5b-faef77f6fc2d · outbound

This paper cites SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators.

Graph Generative Pre-trained Transformer SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators

Reference 25

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Observation 020825da-7d3c-4489-a5bc-d35886933443 · outbound

This paper cites Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models.

Graph Generative Pre-trained Transformer Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

Reference 26

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Observation 6affaa0b-1d1a-4109-a0ba-0348ea5e3451 · outbound

This paper cites Fr\'echet ChemNet Distance: A metric for generative models for molecules in drug discovery.

Graph Generative Pre-trained Transformer Fr\'echet ChemNet Distance: A metric for generative models for molecules in drug discovery

Reference 27

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Observation 26ef5aa0-539c-4f1b-9357-68e0e3153ed6 · outbound

This paper cites DeFoG: Discrete Flow Matching for Graph Generation.

Graph Generative Pre-trained Transformer DeFoG: Discrete Flow Matching for Graph Generation

Reference 28

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Observation ecb31092-e840-4a34-8854-1d655b1f8be9 · outbound

This paper cites High-Dimensional Continuous Control Using Generalized Advantage Estimation.

Graph Generative Pre-trained Transformer High-Dimensional Continuous Control Using Generalized Advantage Estimation

Reference 29

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Observation 804966e8-c8e8-428a-bf15-db9f09bb2602 · outbound

This paper cites and Komodakis, N.

Graph Generative Pre-trained Transformer and Komodakis, N

Reference 31

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Observation f7db2c65-4c29-4b69-b643-b7ff1138b7a1 · outbound

This paper cites Cometh: A continuous-time discrete-state graph diffusion model.

Graph Generative Pre-trained Transformer Cometh: A continuous-time discrete-state graph diffusion model

Reference 32

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Graph Generative Pre-trained Transformer Unresolved cited work

Reference 33

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Observation 07653694-7029-4ae4-84d7-0978f1c0e1a7 · outbound

This paper cites MoleculeNet: A Benchmark for Molecular Machine Learning.

Graph Generative Pre-trained Transformer MoleculeNet: A Benchmark for Molecular Machine Learning

Reference 36

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Observation d469a268-fc16-4ccd-84d1-8f450740a246 · outbound

This paper cites Discrete-state Continuous-time Diffusion for Graph Generation.

Graph Generative Pre-trained Transformer Discrete-state Continuous-time Diffusion for Graph Generation

Reference 37

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Observation 71032502-6f1f-46d5-81e1-33d6559571bb · outbound

This paper cites Graph Contrastive Learning with Augmentations.

Graph Generative Pre-trained Transformer Graph Contrastive Learning with Augmentations

Reference 38

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Observation f044fccd-72fc-4a91-a2c2-9fb108d29807 · outbound

This paper cites Secrets of RLHF in Large Language Models Part I: PPO.

Graph Generative Pre-trained Transformer Secrets of RLHF in Large Language Models Part I: PPO

Reference 39

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This paper cites falls off the grid.

Graph Generative Pre-trained Transformer falls off the grid

Reference 40

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Graph Generative Pre-trained Transformer Unresolved cited work

Reference 41

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This paper cites Training transformers on adjacency matrices.

Graph Generative Pre-trained Transformer Training transformers on adjacency matrices

Reference 42

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Graph Generative Pre-trained Transformer Unresolved cited work

Reference 43

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This paper cites Fine-tuning G2PT for Graph Property Prediction Datasets.

Graph Generative Pre-trained Transformer Fine-tuning G2PT for Graph Property Prediction Datasets

Reference 44

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Observation 9318ddc5-5300-4b42-9d27-5e4700be6367 · outbound

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Graph Generative Pre-trained Transformer Unresolved cited work

Reference 46

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Observation a81baf9e-93de-48d9-84cf-9b8844223778 · outbound

This paper cites The dataset-agnostic metrics evaluate the alignment between the distributions of the generated graphs and the training data by analyzing general graph properties.

Graph Generative Pre-trained Transformer The dataset-agnostic metrics evaluate the alignment between the distributions of the generated graphs and the training data by analyzing general graph properties

Reference 47

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Observation 37d538d2-3e0b-4df1-a169-7fcbfe3e4543 · outbound

This paper cites On the contrary, 19 Graph Generative Pre-trained Transformer Algorithm 4 Depth-First search edge order generation Input: Graph G = (V, E), neighborhood function Nei.(·).

Graph Generative Pre-trained Transformer On the contrary, 19 Graph Generative Pre-trained Transformer Algorithm 4 Depth-First search edge order generation Input: Graph G = (V, E), neighborhood function Nei.(·)

Reference 48

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Graph Generative Pre-trained Transformer Unresolved cited work

Reference 49

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This paper cites Nevertheless, one-shot graph generative models often suffer from the decoding strategies such that it requires an expressive decoder to map from latent vectors to graphs.

Graph Generative Pre-trained Transformer Nevertheless, one-shot graph generative models often suffer from the decoding strategies such that it requires an expressive decoder to map from latent vectors to graphs

Reference 50

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Graph Generative Pre-trained Transformer As discussed in ??, they often require a prefixed number of refinement steps and they need to maintain an adjacency matrix over the trajectory which is computationally intensive

Reference 52

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This paper cites Proximal Policy Optimization Algorithms.

Graph Generative Pre-trained Transformer Proximal Policy Optimization Algorithms

Reference 2015

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Graph Generative Pre-trained Transformer DiGress: Discrete Denoising diffusion for graph generation

Reference 2017

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Graph Generative Pre-trained Transformer BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2018

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This paper cites doi: 10.1021/acs.jcim.8b00839.

Graph Generative Pre-trained Transformer doi: 10.1021/acs.jcim.8b00839

Reference 2019

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Graph Generative Pre-trained Transformer NVDiff: Graph Generation through the Diffusion of Node Vectors

Reference 2021

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This paper cites Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design.

Graph Generative Pre-trained Transformer Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design

Reference 2022

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Graph Generative Pre-trained Transformer An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 2023

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Graph Generative Pre-trained Transformer Efficient and Scalable Graph Generation through Iterative Local Expansion

Reference 2024

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This paper cites MADGEN: Mass-Spec attends to De Novo Molecular generation.

Graph Generative Pre-trained Transformer MADGEN: Mass-Spec attends to De Novo Molecular generation

Reference 2025

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Pith citing papers

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Retrieval-Augmented Generation with Graphs (GraphRAG) cites this paper.

Retrieval-Augmented Generation with Graphs (GraphRAG) Graph Generative Pre-trained Transformer

Reference 52

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Observation 8ba94831-589c-4d53-afad-4d56b21ee336 · inbound

Scalable Interference Graph Learning for Low-Latency Wi-Fi Networks using Hashing-based Evolution Strategy cites this paper.

Scalable Interference Graph Learning for Low-Latency Wi-Fi Networks using Hashing-based Evolution Strategy Graph Generative Pre-trained Transformer

Reference 31

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NGTM: Substructure-based Neural Graph Topic Model for Interpretable Graph Generation cites this paper.

NGTM: Substructure-based Neural Graph Topic Model for Interpretable Graph Generation Graph Generative Pre-trained Transformer

Reference 12

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PROVCREATOR: Synthesizing Complex Heterogenous Graphs with Node and Edge Attributes cites this paper.

PROVCREATOR: Synthesizing Complex Heterogenous Graphs with Node and Edge Attributes Graph Generative Pre-trained Transformer

Reference 7

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Observation aad9949d-f8a7-4d1b-9944-8563c10a8c7c · inbound

From GPT-3 to GPT-5: Mapping their capabilities, scope, limitations, and consequences cites this paper.

From GPT-3 to GPT-5: Mapping their capabilities, scope, limitations, and consequences Graph Generative Pre-trained Transformer

Reference 11

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