Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks | Paper Citation Record

Observation f63084db-3121-4e75-ad1a-2b7705a2b86a · outbound

This paper cites Relational inductive biases, deep learning, and graph networks.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Relational inductive biases, deep learning, and graph networks

Reference 1

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Observation ea9c76c8-979c-4e84-9b54-559330f32359 · outbound

This paper cites Foundations of data science.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Foundations of data science

Reference 2

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Observation 6e851380-d256-4816-bf2d-4f92c0c222b0 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks On the Opportunities and Risks of Foundation Models

Reference 3

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This paper cites Translating embeddings for modeling multi-relational data.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Translating embeddings for modeling multi-relational data

Reference 4

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This paper cites Shortest-path kernels on graphs.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Shortest-path kernels on graphs

Reference 5

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

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks GraphLLM: Boosting Graph Reasoning Ability of Large Language Model

Reference 6

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This paper cites Structure-aware transformer for graph representation learning.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Structure-aware transformer for graph representation learning

Reference 7

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This paper cites Learning Long Range Dependencies on Graphs via Random Walks.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Learning Long Range Dependencies on Graphs via Random Walks

Reference 8

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Observation 5d7ecffd-e5d9-4a7a-8109-78b4657d59a8 · outbound

This paper cites FastGCN: Fast learning with graph convolutional networks via importance sampling.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks FastGCN: Fast learning with graph convolutional networks via importance sampling

Reference 9

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This paper cites NAGphormer: A tokenized graph transformer for node classification in large graphs.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks NAGphormer: A tokenized graph transformer for node classification in large graphs

Reference 10

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This paper cites Simple and deep graph convolutional networks.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Simple and deep graph convolutional networks

Reference 11

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This paper cites LLaGA: Large Language and Graph Assistant.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks LLaGA: Large Language and Graph Assistant

Reference 12

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This paper cites Exploring the potential of large language models (LLMs) in learning on graph.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Exploring the potential of large language models (LLMs) in learning on graph

Reference 13

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Observation ca912c27-6ad2-4852-848d-0b80d1905660 · outbound

This paper cites Node feature extraction by self-supervised multi-scale neighborhood prediction.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Node feature extraction by self-supervised multi-scale neighborhood prediction

Reference 14

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This paper cites Rethinking attention with performers.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Rethinking attention with performers

Reference 15

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This paper cites Convolutional 2d knowledge graph embeddings.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Convolutional 2d knowledge graph embeddings

Reference 16

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This paper cites A generalization of transformer networks to graphs.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks A generalization of transformer networks to graphs

Reference 17

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This paper cites Long range graph benchmark.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Long range graph benchmark

Reference 18

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This paper cites Talk like a graph: Encoding graphs for large language models.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Talk like a graph: Encoding graphs for large language models

Reference 19

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This paper cites Towards Foundation Models for Knowledge Graph Reasoning.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Towards Foundation Models for Knowledge Graph Reasoning

Reference 20

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This paper cites Predict then propagate: Graph neural networks meet personalized pagerank.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Predict then propagate: Graph neural networks meet personalized pagerank

Reference 21

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This paper cites Chembl: a large-scale bioactivity database for drug discovery.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Chembl: a large-scale bioactivity database for drug discovery

Reference 22

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This paper cites Citeseer: An automatic citation indexing system.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Citeseer: An automatic citation indexing system

Reference 23

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This paper cites Schoenholz, Patrick F.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Schoenholz, Patrick F

Reference 24

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This paper cites node2vec: Scalable feature learning for networks.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks node2vec: Scalable feature learning for networks

Reference 25

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This paper cites GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 26

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This paper cites Gutmann and Aapo Hyvärinen.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Gutmann and Aapo Hyvärinen

Reference 27

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This paper cites Hamilton, Rex Ying, and Jure Leskovec.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Hamilton, Rex Ying, and Jure Leskovec

Reference 28

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This paper cites Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning

Reference 30

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This paper cites Unigraph: Learning a cross-domain graph foundation model from natural language.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Unigraph: Learning a cross-domain graph foundation model from natural language

Reference 31

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This paper cites Learning deep representations by mutual information estimation and maximization.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Learning deep representations by mutual information estimation and maximization

Reference 32

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This paper cites Training Compute-Optimal Large Language Models.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Training Compute-Optimal Large Language Models

Reference 33

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

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks GraphMAE: Self-supervised masked graph autoencoders

Reference 34

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

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Open Graph Benchmark: Datasets for Machine Learning on Graphs

Reference 35

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Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Strategies for pre-training graph neural networks

Reference 36

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This paper cites PRODIGY: Enabling in-context learning over graphs.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks PRODIGY: Enabling in-context learning over graphs

Reference 37

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This paper cites Scaling Laws for Neural Language Models.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Scaling Laws for Neural Language Models

Reference 38

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

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 39

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Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Kipf and Max Welling

Reference 40

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This paper cites Hamilton, Vincent Létourneau, and Prudencio Tossou.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Hamilton, Vincent Létourneau, and Prudencio Tossou

Reference 41

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

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks What’s behind the mask: Understanding masked graph modeling for graph autoencoders

Reference 42

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This paper cites Deeper insights into graph convolutional networks for semi-supervised learning.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Deeper insights into graph convolutional networks for semi-supervised learning

Reference 43

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This paper cites Gated graph sequence neural networks.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Gated graph sequence neural networks

Reference 44

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Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks One for all: Towards training one graph model for all classification tasks

Reference 45

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

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Graph Foundation Models: Concepts, Opportunities and Challenges

Reference 46

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This paper cites Generating Wikipedia by Summarizing Long Sequences.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Generating Wikipedia by Summarizing Long Sequences

Reference 47

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This paper cites Graphprompt: Unifying pre-training and downstream tasks for graph neural networks.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Graphprompt: Unifying pre-training and downstream tasks for graph neural networks

Reference 48

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Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Unresolved cited work

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Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Decoupled Weight Decay Regularization

Reference 50

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Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Dokania, Mark Coates, Philip Torr, and Ser-Nam Lim

Reference 51

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Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Position: Graph foundation models are already here

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Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Graph reconstruction via distance oracles

Reference 53

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This paper cites Automating the construction of internet portals with machine learning.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Automating the construction of internet portals with machine learning

Reference 54

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Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks

Reference 55

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This paper cites Graph-to-Graph Transformer for Transition-based Dependency Parsing.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Graph-to-Graph Transformer for Transition-based Dependency Parsing

Reference 56

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This paper cites Recursive non-autoregressive graph-to-graph transformer for dependency parsing with iterative refinement.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Recursive non-autoregressive graph-to-graph transformer for dependency parsing with iterative refinement

Reference 57

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This paper cites DeepWalk: Online learning of social representations.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks DeepWalk: Online learning of social representations

Reference 58

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This paper cites Gcc: Graph contrastive coding for graph neural network pre-training.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Gcc: Graph contrastive coding for graph neural network pre-training

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:00:59.030587Z

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Observation 0ef6ca97-a42e-442e-b8f4-77b900112675 · outbound

This paper cites Improving language understanding by generative pre-training.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Improving language understanding by generative pre-training

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:00:59.012834Z

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Observation bc3bb65c-3a01-42a2-a3af-d105a822e557 · outbound

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

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Recipe for a general, powerful, scalable graph transformer

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:00:58.998767Z

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Observation 89270f6d-c9eb-48f7-9157-e3914af4e005 · outbound

This paper cites Learning and verifying graphs using queries with a focus on edge counting.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Learning and verifying graphs using queries with a focus on edge counting

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:00:58.983638Z

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Observation d68b6481-8dac-406e-a851-512f7f0ca65d · outbound

This paper cites DropEdge: Towards deep graph convolutional networks on node classification.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks DropEdge: Towards deep graph convolutional networks on node classification

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:00:58.969028Z

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Observation 5c55d825-0e28-46f7-b6c7-8c517aeaefb5 · outbound

This paper cites GPPT: Graph pre-training and prompt tuning to generalize graph neural networks.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks GPPT: Graph pre-training and prompt tuning to generalize graph neural networks

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:00:58.953868Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 036d28a0-4c79-4d04-a766-fa8be04159e4 · outbound

This paper cites All in one: Multi-task prompting for graph neural networks.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks All in one: Multi-task prompting for graph neural networks

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-15T20:00:58.084045Z

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Observation a74bb996-f7ee-4023-b532-d8bb032b23dd · outbound

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

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Walklm: A uniform language model fine-tuning framework for attributed graph embedding

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:00:58.928516Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 344e9a19-ba0a-4e29-999f-6110a9851e7c · outbound

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

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Graphgpt: Graph instruction tuning for large language models

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:00:58.912144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d1d3a92e-52c6-4f41-a376-e8c976e0df70 · outbound

This paper cites Dyer, Rémi Munos, Petar Veliˇckovi´c, and Michal Valko.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Dyer, Rémi Munos, Petar Veliˇckovi´c, and Michal Valko

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:00:58.897008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0a2766d4-14d1-4c50-a496-b6f6bf0c00e2 · outbound

This paper cites Chawla, and Panpan Xu.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Chawla, and Panpan Xu

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:00:58.881895Z

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Observation c7cc2f2b-68f8-41eb-a328-8ac7a9e7237a · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-15T20:00:58.107638Z

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Observation bf21e75e-4f2c-42bd-a422-b2f5f6886c46 · outbound

This paper cites Gomez, Lukasz Kaiser, and Illia Polosukhin.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-15T20:00:58.113101Z

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Observation 5bed2888-2965-41ec-896b-ad086008e923 · outbound

This paper cites Graph attention networks.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Graph attention networks

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-15T20:00:58.118216Z

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Observation 477462b9-e2e2-49c1-9021-70733fdc90be · outbound

This paper cites Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-15T20:00:58.122529Z

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Unavailable: canonical work link unavailable.

Observation bb6d147f-e387-4584-a6f1-899646083e40 · outbound

This paper cites Can language models solve graph problems in natural language? In NeurIPS, 2023.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Can language models solve graph problems in natural language? In NeurIPS, 2023

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:00:58.839728Z

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Observation c73ee7ec-a2e7-4026-876d-b778174d1c6d · outbound

This paper cites GFT: Graph foundation model with transferable tree vocabulary.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks GFT: Graph foundation model with transferable tree vocabulary

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:00:58.825403Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 68498aee-a252-4241-82d0-a01aec4dc61a · outbound

This paper cites Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:00:58.810271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c8a97882-ef1c-44cb-ab3b-0bd5d6d85beb · outbound

This paper cites NodeFormer: A scalable graph structure learning transformer for node classification.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks NodeFormer: A scalable graph structure learning transformer for node classification

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:00:58.796178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ef65b827-8a32-42f5-8b6a-ef56df412d20 · outbound

This paper cites Wright, Azalia Mirhoseini, Joseph E.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Wright, Azalia Mirhoseini, Joseph E

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-15T20:00:58.145379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e47797ad-0716-4b43-b770-b617dae8af88 · outbound

This paper cites Moleculenet: a benchmark for molecular machine learning.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Moleculenet: a benchmark for molecular machine learning

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-15T20:00:58.149981Z

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Observation 8ff08a91-34cc-40ff-bc7a-0d37030770df · outbound

This paper cites OpenGraph: Towards Open Graph Foundation Models.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks OpenGraph: Towards Open Graph Foundation Models

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-15T20:00:58.154365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0871e673-56a3-4d37-b39e-3e970463aa67 · outbound

This paper cites How powerful are graph neural networks? In ICLR, 2019.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks How powerful are graph neural networks? In ICLR, 2019

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-15T20:00:58.158525Z

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Unavailable: canonical work link unavailable.

Observation 724b74f8-c2a5-40d8-a41e-a009340706e1 · outbound

This paper cites Revisiting semi-supervised learning with graph embeddings.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Revisiting semi-supervised learning with graph embeddings

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-15T20:00:58.162781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 727dfc3b-93ca-4f31-861d-7b93cf6580b6 · outbound

This paper cites Do transformers really perform bad for graph representation? In NeurIPS, 2021.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Do transformers really perform bad for graph representation? In NeurIPS, 2021

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:00:58.733019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 285f48b0-a982-42cf-922e-ec6ad94f410b · outbound

This paper cites Graph contrastive learning with augmentations.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Graph contrastive learning with augmentations

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:00:58.715099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 217659f6-d6c1-4f11-ad5c-456bdccac9bc · outbound

This paper cites Hierarchical graph transformer with adaptive node sampling.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Hierarchical graph transformer with adaptive node sampling

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:00:58.699509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 78a06ecd-2b87-41e7-b761-b19f071953ed · outbound

This paper cites All in one and one for all: A simple yet effective method towards cross-domain graph pretraining.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks All in one and one for all: A simple yet effective method towards cross-domain graph pretraining

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:00:58.683932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1534a4e0-4ba4-4c8d-8a42-e89c0b749123 · outbound

This paper cites Gophormer: Ego-Graph Transformer for Node Classification.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Gophormer: Ego-Graph Transformer for Node Classification

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-15T20:00:58.184992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57f276dd-a0e5-4e54-b9d8-35f8369ce1b1 · outbound

This paper cites GraphText: Graph Reasoning in Text Space.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks GraphText: Graph Reasoning in Text Space

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-15T20:00:58.189903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 035ff7af-7698-4a92-8d3a-ea15f743c065 · outbound

This paper cites Hierarchical transformer for scalable graph learning.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Hierarchical transformer for scalable graph learning

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:00:58.669859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6261c744-681a-4094-be99-7f6c5be34dc3 · outbound

This paper cites Deep Graph Contrastive Representation Learning.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Deep Graph Contrastive Representation Learning

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-15T20:00:58.200249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c9098cd-d042-4f6c-9ce6-8491343164e7 · outbound

This paper cites Graph contrastive learning with adaptive augmentation.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Graph contrastive learning with adaptive augmentation

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:00:58.655229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9c244d28-eaab-4e94-88cb-22de1d1bdda9 · outbound

This paper cites an unresolved cited work.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks Unresolved cited work

Reference 92

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:00:58.640587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2bd8fe55-a546-4c23-87ba-440410cc786a · outbound

This paper cites PAPER TITLE AND ABSTRACT:.

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks PAPER TITLE AND ABSTRACT:

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:00:58.624903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers