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Paper Citation Record · LEDGER

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning

As of 15 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2605.01310.

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

pith.paper-citation-record.v1
2605.01310 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-09T14:36:29.620205Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

29 of 29 outbound references displayed

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  • verified fuzzy26
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 37a4de6a-61d7-44c5-bbd0-34779374712c · outbound

This paper cites SciBERT: A pretrained language model for scientific text.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning SciBERT: A pretrained language model for scientific text

Reference 1

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Observation cd8125f4-0666-4c70-9b30-d3ca6ee34836 · outbound

This paper cites Beyond efficiency: Molecular data pruning for enhanced generalization.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning Beyond efficiency: Molecular data pruning for enhanced generalization

Reference 2

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Observation cbb39de4-e92f-472d-ac59-e8a87af1d456 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 3

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Observation c9a42204-17be-41b6-8b38-85264afbc925 · outbound

This paper cites Mirage: Model-agnostic graph distillation for graph classification.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning Mirage: Model-agnostic graph distillation for graph classification

Reference 4

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation b3714f0a-32d0-4346-8333-5dff97080d26 · outbound

This paper cites Graphmae: Self-supervised masked graph autoencoders.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning Graphmae: Self-supervised masked graph autoencoders

Reference 5

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Observation bc01232b-4632-42c8-a5f7-1dd89fe9ebbf · outbound

This paper cites Graphmae2: A decoding-enhanced masked self-supervised graph learner.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning Graphmae2: A decoding-enhanced masked self-supervised graph learner

Reference 6

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Observation c090f555-c509-43e3-b91c-16ecb461ed56 · outbound

This paper cites Open Graph Benchmark: Datasets for Machine Learning on Graphs.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning Open Graph Benchmark: Datasets for Machine Learning on Graphs

Reference 7

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Observation 26775c8c-8283-4324-89e1-71f909d684a4 · outbound

This paper cites What’s behind the mask: Understanding masked graph modeling for graph autoen- coders.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning What’s behind the mask: Understanding masked graph modeling for graph autoen- coders

Reference 8

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Source-reported events for the cited work

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Observation f9e388fa-61bf-4c8c-a05a-221408eec8e6 · outbound

This paper cites Model-free graph data selection under distribution shift.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning Model-free graph data selection under distribution shift

Reference 9

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Source-reported events for the cited work

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Observation 9eef1ffd-1c79-433b-96bc-7d2e56339a79 · outbound

This paper cites Rethinking tokenizer and decoder in masked graph mod- eling for molecules.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning Rethinking tokenizer and decoder in masked graph mod- eling for molecules

Reference 10

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Source-reported events for the cited work

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Observation 77c2cd73-43a2-4e4b-84b7-16adf6daa809 · outbound

This paper cites Where to mask: Structure-guided masking for graph masked autoen- coders.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning Where to mask: Structure-guided masking for graph masked autoen- coders

Reference 11

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Source-reported events for the cited work

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Observation 209f5b36-cb0a-42c9-af45-ab624049b08b · outbound

This paper cites Graph positional autoen- coders as self-supervised learners.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning Graph positional autoen- coders as self-supervised learners

Reference 12

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Source-reported events for the cited work

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Observation db22a2ef-f1ec-4a6e-b998-2806cbab522d · outbound

This paper cites Hi-gmae: Hierarchical graph masked autoencoders.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning Hi-gmae: Hierarchical graph masked autoencoders

Reference 13

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Source-reported events for the cited work

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Observation 401f8108-0e9a-49a1-b02f-f365fc3e7280 · outbound

This paper cites Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann

Reference 14

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Source-reported events for the cited work

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Observation 6da8f687-68d5-4324-9a48-088e92776444 · outbound

This paper cites Biot5: Enriching cross-modal integration in biology with chemical knowledge and natural language associations.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning Biot5: Enriching cross-modal integration in biology with chemical knowledge and natural language associations

Reference 15

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Source-reported events for the cited work

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Observation 08a70653-68ca-49f0-a92b-f92447af8e64 · outbound

This paper cites Recipe for a general, powerful, scalable graph transformer.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning Recipe for a general, powerful, scalable graph transformer

Reference 16

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Source-reported events for the cited work

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Observation fd203373-6201-4bb5-a408-1a46bf956180 · outbound

This paper cites Adversarial contrastive graph masked autoencoder against graph structure and feature dual attacks.Proceed- ings of the AAAI Conference on Artificial Intelligence.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning Adversarial contrastive graph masked autoencoder against graph structure and feature dual attacks.Proceed- ings of the AAAI Conference on Artificial Intelligence

Reference 17

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Source-reported events for the cited work

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Observation 1ce62a69-0db5-41d8-829d-34dc6c8b6601 · outbound

This paper cites Gigamae: Generalizable graph masked autoencoder via collaborative latent space reconstruction.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning Gigamae: Generalizable graph masked autoencoder via collaborative latent space reconstruction

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation f53c590b-9415-4047-adad-65f4473a4a33 · outbound

This paper cites Zinc 15–ligand discovery for everyone.J.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning Zinc 15–ligand discovery for everyone.J

Reference 19

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Source-reported events for the cited work

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Observation 025a420d-5350-4cba-81c0-ffe96f3848c8 · outbound

This paper cites S2gae: Self-supervised graph autoencoders are generalizable learn- ers with graph masking.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning S2gae: Self-supervised graph autoencoders are generalizable learn- ers with graph masking

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation d332e922-3b95-4540-80dc-3dc10968dfd5 · outbound

This paper cites Rethinking graph masked autoencoders through alignment and uniformity.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning Rethinking graph masked autoencoders through alignment and uniformity

Reference 21

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Source-reported events for the cited work

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Observation aa4eb79a-d0e8-46fc-8c0e-be7dd2e8f21a · outbound

This paper cites Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S

Reference 22

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Source-reported events for the cited work

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Observation 5f390eb9-61bd-41ce-a6e5-4254c3e7d3d5 · outbound

This paper cites an unresolved cited work.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning Unresolved cited work

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 563581d0-380e-4e4e-81bc-25cec9187448 · outbound

This paper cites A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications

Reference 24

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verified exact
arxiv_id, observed 2026-05-11T16:51:09.440572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation ebdfc0f1-38ee-45ea-8ed1-ff9289fe22df · outbound

This paper cites Does graph distillation see like vision dataset counter- part? InThirty-seventh Conference on Neural Information Processing Systems.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning Does graph distillation see like vision dataset counter- part? InThirty-seventh Conference on Neural Information Processing Systems

Reference 25

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation fe6e64f0-95c4-47e0-bea3-45b92e30520f · outbound

This paper cites Do transformers really perform badly for graph representation? InConf.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning Do transformers really perform badly for graph representation? InConf

Reference 26

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 184b0319-0da7-4ae4-8cb9-214b5c038b6a · outbound

This paper cites Graph con- trastive learning with augmentations.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning Graph con- trastive learning with augmentations

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 95cfdbe9-b166-49e9-8ca5-25b4ee5eb3f2 · outbound

This paper cites GDer: Safeguarding efficiency, balancing, and robustness via pro- totypical graph pruning.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning GDer: Safeguarding efficiency, balancing, and robustness via pro- totypical graph pruning

Reference 28

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verified fuzzy
raw_fallback, observed 2026-05-26T01:56:30.122960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 8b4690e9-73bc-4141-9bca-1f96d2d6fe9e · outbound

This paper cites Protom- gae: Prototype-aware masked graph auto-encoder for graph representation learning.ACM Trans.

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning Protom- gae: Prototype-aware masked graph auto-encoder for graph representation learning.ACM Trans

Reference 29

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verified fuzzy
raw_fallback, observed 2026-05-26T01:56:30.126349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Pith citing papers

No inbound Pith citation observations are available.