Paper Citation Record · LEDGER

Masked Language Models are Good Heterogeneous Graph Generalizers

As of 14 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2506.06157.

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

pith.paper-citation-record.v1
2506.06157 v2

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:04:17.378893Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

48 of 48 outbound references displayed

verified exact2
verified fuzzy24
unresolved22
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ad6eb98d-8d0b-480c-bcdc-6e1d1d00da27 · outbound

This paper cites GPT-4 Technical Report.

Masked Language Models are Good Heterogeneous Graph Generalizers GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:11.720853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 664b7213-7a60-4590-a29a-636d1bbb47fa · outbound

This paper cites Graph markup language (graphml).

Masked Language Models are Good Heterogeneous Graph Generalizers Graph markup language (graphml)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:24.656007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation db1be566-bd7c-497c-85ba-9a1257929c18 · outbound

This paper cites D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.

Masked Language Models are Good Heterogeneous Graph Generalizers D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:24.313068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2898b609-1160-4732-9b2d-f17b598a0e59 · outbound

This paper cites Lmbot: distilling graph knowledge into language model for graph-less deployment in twitter bot detection.

Masked Language Models are Good Heterogeneous Graph Generalizers Lmbot: distilling graph knowledge into language model for graph-less deployment in twitter bot detection

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:24.032214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 06a8f34c-d885-46b5-a80f-36496bf3bbc3 · outbound

This paper cites GraphLLM: Boosting Graph Reasoning Ability of Large Language Model.

Masked Language Models are Good Heterogeneous Graph Generalizers GraphLLM: Boosting Graph Reasoning Ability of Large Language Model

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:12.306341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e983ba50-91ab-4213-be51-42dff174a4a4 · outbound

This paper cites Heterogeneous graph contrastive learning for recommendation.

Masked Language Models are Good Heterogeneous Graph Generalizers Heterogeneous graph contrastive learning for recommendation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:23.705023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 01e108d0-e4e5-4219-8a7d-03de13845e2c · outbound

This paper cites LLaGA: Large Language and Graph Assistant.

Masked Language Models are Good Heterogeneous Graph Generalizers LLaGA: Large Language and Graph Assistant

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:12.654691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd066ee8-2383-4b0a-a53d-a24345d86082 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Masked Language Models are Good Heterogeneous Graph Generalizers BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:12.846933Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0247295-f74b-49cc-8009-6fd38c922424 · outbound

This paper cites V ., and Swami, A.

Masked Language Models are Good Heterogeneous Graph Generalizers V ., and Swami, A

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:23.452703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1feabc73-e757-4beb-ac08-d4e02f66d1b6 · outbound

This paper cites Fast Graph Representation Learning with PyTorch Geometric.

Masked Language Models are Good Heterogeneous Graph Generalizers Fast Graph Representation Learning with PyTorch Geometric

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:13.113175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8991bb65-da53-47c8-8c9e-c9acc76ccbf7 · outbound

This paper cites Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding.

Masked Language Models are Good Heterogeneous Graph Generalizers Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:23.214437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c95c33cf-a191-470d-99c2-544c8c1ac25d · outbound

This paper cites Gml: A portable graph file format.

Masked Language Models are Good Heterogeneous Graph Generalizers Gml: A portable graph file format

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:23.044166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ff319034-c37f-418d-9df0-55161b53836e · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Masked Language Models are Good Heterogeneous Graph Generalizers LoRA: Low-Rank Adaptation of Large Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:13.523647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed8d495e-a568-485d-b56f-b3a12cd4ab07 · outbound

This paper cites Gpt-gnn: Generative pre-training of graph neural networks.

Masked Language Models are Good Heterogeneous Graph Generalizers Gpt-gnn: Generative pre-training of graph neural networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:22.869128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a809e76c-c36c-4765-97eb-f68feae38cfe · outbound

This paper cites Heterogeneous graph transformer.

Masked Language Models are Good Heterogeneous Graph Generalizers Heterogeneous graph transformer

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:22.714767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4a56b4d5-7ca3-4e1f-a3c8-ed0b6ee972c5 · outbound

This paper cites Pre-training on large-scale heterogeneous graph.

Masked Language Models are Good Heterogeneous Graph Generalizers Pre-training on large-scale heterogeneous graph

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:22.570652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 081f123d-e31f-4da4-bbe6-43f1498b2219 · outbound

This paper cites X., and Li, J.

Masked Language Models are Good Heterogeneous Graph Generalizers X., and Li, J

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:22.398684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b3428b61-e21d-4168-a2a4-ebcf997720f1 · outbound

This paper cites LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model.

Masked Language Models are Good Heterogeneous Graph Generalizers LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:14.191133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a058a99-bfd4-4e91-89ec-244273713cb2 · outbound

This paper cites One for All: Towards Training One Graph Model for All Classification Tasks.

Masked Language Models are Good Heterogeneous Graph Generalizers One for All: Towards Training One Graph Model for All Classification Tasks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:14.300110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afab44a1-1e26-40b8-900d-afe9420f714e · outbound

This paper cites Graph Foundation Models: Concepts, Opportunities and Challenges.

Masked Language Models are Good Heterogeneous Graph Generalizers Graph Foundation Models: Concepts, Opportunities and Challenges

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:14.422754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4e813f1-3c5f-4118-ba7a-89805474ede5 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Masked Language Models are Good Heterogeneous Graph Generalizers RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:14.539396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5fbf0ee5-321a-4283-92c5-7de099c17da4 · outbound

This paper cites an unresolved cited work.

Masked Language Models are Good Heterogeneous Graph Generalizers Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-07T06:04:22.246081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d21e02b2-d09a-4c73-b686-216d01c95c6c · outbound

This paper cites Decoupled Weight Decay Regularization.

Masked Language Models are Good Heterogeneous Graph Generalizers Decoupled Weight Decay Regularization

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:14.737520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a33d7a6d-6dad-4408-b70a-03a36bfed889 · outbound

This paper cites Relation structure-aware heterogeneous information network embedding.

Masked Language Models are Good Heterogeneous Graph Generalizers Relation structure-aware heterogeneous information network embedding

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:22.052602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 858ecbc0-090e-41d7-a241-ffc682b82155 · outbound

This paper cites Single- cell biological network inference using a heterogeneous graph transformer.

Masked Language Models are Good Heterogeneous Graph Generalizers Single- cell biological network inference using a heterogeneous graph transformer

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:21.914783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 53aff4df-0511-4a15-bec4-d74dbaf0e0d4 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Masked Language Models are Good Heterogeneous Graph Generalizers Pytorch: An imperative style, high-performance deep learning library

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:15.151480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5fdf1c2-7772-4309-b85d-f321323b0f35 · outbound

This paper cites Scikit-learn: Machine learning in python.the Journal of machine Learning research, 12:2825–2830, 2011.

Masked Language Models are Good Heterogeneous Graph Generalizers Scikit-learn: Machine learning in python.the Journal of machine Learning research, 12:2825–2830, 2011

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:21.722092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a8e4c835-ec85-41c0-bb32-038ed60b3216 · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Masked Language Models are Good Heterogeneous Graph Generalizers DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:15.418693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f89961fa-bc01-4f46-94f4-8dbbd58ac524 · outbound

This paper cites N., Bloem, P., Van Den Berg, R., Titov, I., and Welling, M.

Masked Language Models are Good Heterogeneous Graph Generalizers N., Bloem, P., Van Den Berg, R., Titov, I., and Welling, M

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:21.576653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e5c2e98f-31c8-4ae2-83d4-5860477a229e · outbound

This paper cites RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space.

Masked Language Models are Good Heterogeneous Graph Generalizers RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:15.630932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b5f4977-a5b2-4611-9cbe-5ef50274f7c2 · outbound

This paper cites Walklm: A uniform language model fine-tuning framework for attributed graph embedding.

Masked Language Models are Good Heterogeneous Graph Generalizers Walklm: A uniform language model fine-tuning framework for attributed graph embedding

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:21.382439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c1f6a188-fc0b-46ca-9f88-6d4e400d3487 · outbound

This paper cites Graphgpt: Graph instruction tuning for large language models.

Masked Language Models are Good Heterogeneous Graph Generalizers Graphgpt: Graph instruction tuning for large language models

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:21.203887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b0530591-94a0-49c2-9858-b7d5350c9994 · outbound

This paper cites Higpt: Heterogeneous graph language model.

Masked Language Models are Good Heterogeneous Graph Generalizers Higpt: Heterogeneous graph language model

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:20.910741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 48380e5b-95cb-4496-bfe6-8a547d1499a3 · outbound

This paper cites an unresolved cited work.

Masked Language Models are Good Heterogeneous Graph Generalizers Unresolved cited work

Reference 34

Resolution: unresolved; raw_fallback, observed 2026-08-07T06:04:20.537697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d26f3c8c-449c-4d3a-897d-7da215acd7fe · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Masked Language Models are Good Heterogeneous Graph Generalizers LLaMA: Open and Efficient Foundation Language Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:16.248050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efd70c6a-14f9-4542-989f-a0c484b12d07 · outbound

This paper cites Can language models solve graph problems in natural language? Advances in Neural Information Processing Systems, 36, 2024.

Masked Language Models are Good Heterogeneous Graph Generalizers Can language models solve graph problems in natural language? Advances in Neural Information Processing Systems, 36, 2024

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:20.205072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0b47e632-bcf1-49a5-ba95-04957683d64c · outbound

This paper cites Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks.

Masked Language Models are Good Heterogeneous Graph Generalizers Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:16.383986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4d25ad3-22ee-4d7b-a0e3-2b9851dcf8f4 · outbound

This paper cites an unresolved cited work.

Masked Language Models are Good Heterogeneous Graph Generalizers Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-08-07T06:04:19.934106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4442dc53-75ff-4a5f-ac1b-a9ca4cc1bcaa · outbound

This paper cites Self-supervised heterogeneous graph neural network with co- contrastive learning.

Masked Language Models are Good Heterogeneous Graph Generalizers Self-supervised heterogeneous graph neural network with co- contrastive learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:19.630302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 13a7926d-6a12-4839-a01c-ce821c6b7f3a · outbound

This paper cites an unresolved cited work.

Masked Language Models are Good Heterogeneous Graph Generalizers Unresolved cited work

Reference 40

Resolution: unresolved; raw_fallback, observed 2026-08-07T06:04:19.259932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation be39b3ef-dd10-4200-9f47-f9c7f558031e · outbound

This paper cites Transformers: State-of-the-art natural language processing.

Masked Language Models are Good Heterogeneous Graph Generalizers Transformers: State-of-the-art natural language processing

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:18.879195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c10f6d60-0e90-4c96-8b3e-38fab55ec22e · outbound

This paper cites Qwen3 Technical Report.

Masked Language Models are Good Heterogeneous Graph Generalizers Qwen3 Technical Report

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:16.776361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff292f52-685f-4764-942b-8fd4f0c5dfe0 · outbound

This paper cites Heterogeneous network representation learning: A unified framework with survey and benchmark.

Masked Language Models are Good Heterogeneous Graph Generalizers Heterogeneous network representation learning: A unified framework with survey and benchmark

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:18.531174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e1e3fc63-6743-462f-a008-931cc8cc9596 · outbound

This paper cites Interpretable and efficient heterogeneous graph convolutional network.

Masked Language Models are Good Heterogeneous Graph Generalizers Interpretable and efficient heterogeneous graph convolutional network

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:18.286341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fc2e0879-6c9f-4447-99c0-8829e4cc30a0 · outbound

This paper cites Self-supervised heterogeneous graph pre-training based on structural clustering.

Masked Language Models are Good Heterogeneous Graph Generalizers Self-supervised heterogeneous graph pre-training based on structural clustering

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:18.089102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cc088158-b27e-45fe-980d-b54d14bfdd18 · outbound

This paper cites Language is All a Graph Needs.

Masked Language Models are Good Heterogeneous Graph Generalizers Language is All a Graph Needs

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:17.194313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 743caa6b-568e-4984-ad77-44456a33f396 · outbound

This paper cites Look Ahead or Look Around? A Theoretical Comparison Between Autoregressive and Masked Pretraining.

Masked Language Models are Good Heterogeneous Graph Generalizers Look Ahead or Look Around? A Theoretical Comparison Between Autoregressive and Masked Pretraining

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-08-07T06:04:17.824261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1907663f-0d2f-476e-a7fa-bb58505a5455 · outbound

This paper cites Llm as gnn: Graph vocabulary learning for graph foundation model.

Masked Language Models are Good Heterogeneous Graph Generalizers Llm as gnn: Graph vocabulary learning for graph foundation model

Reference 48

Resolution: verified exact; raw_fallback, observed 2026-08-07T06:04:17.608938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

No inbound Pith citation observations are available.