Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees | Paper Citation Record

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This paper cites write newline.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees write newline

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This paper cites L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al

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This paper cites When to pre-train graph neural networks? from data generation perspective! In KDD, 2023.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees When to pre-train graph neural networks? from data generation perspective! In KDD, 2023

Reference 3

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This paper cites K., Shah, N., and Wang, Z.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees K., Shah, N., and Wang, Z

Reference 4

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This paper cites Can graph neural networks count substructures? In NeurIPS, 2020.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Can graph neural networks count substructures? In NeurIPS, 2020

Reference 5

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This paper cites Text-space graph foundation models: Comprehensive benchmarks and new insights.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Text-space graph foundation models: Comprehensive benchmarks and new insights

Reference 6

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This paper cites Fastgas: Fast graph-based annotation selection for in-context learning.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Fastgas: Fast graph-based annotation selection for in-context learning

Reference 7

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This paper cites and Jegelka, S.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees and Jegelka, S

Reference 8

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This paper cites On the generalization ability of unsupervised pretraining.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees On the generalization ability of unsupervised pretraining

Reference 9

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This paper cites Graph prototypical networks for few-shot learning on attributed networks.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Graph prototypical networks for few-shot learning on attributed networks

Reference 10

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This paper cites Learning theory can (sometimes) explain generalisation in graph neural networks.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Learning theory can (sometimes) explain generalisation in graph neural networks

Reference 11

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This paper cites Heterogeneous temporal graph neural network.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Heterogeneous temporal graph neural network

Reference 12

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This paper cites Taglas: An atlas of text-attributed graph datasets in the era of large graph and language models.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Taglas: An atlas of text-attributed graph datasets in the era of large graph and language models

Reference 13

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This paper cites The development of social network analysis.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees The development of social network analysis

Reference 14

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This paper cites Towards foundation models for knowledge graph reasoning.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Towards foundation models for knowledge graph reasoning

Reference 15

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This paper cites Generalization and representational limits of graph neural networks.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Generalization and representational limits of graph neural networks

Reference 16

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This paper cites Gpt4graph: Can large language models understand graph structured data? an empirical evaluation and benchmarking.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Gpt4graph: Can large language models understand graph structured data? an empirical evaluation and benchmarking

Reference 17

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

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Mirage: Model-agnostic graph distillation for graph classification

Reference 18

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This paper cites Inductive representation learning on large graphs.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Inductive representation learning on large graphs

Reference 19

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This paper cites A., and Jin, W.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees A., and Jin, W

Reference 20

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This paper cites Masked autoencoders are scalable vision learners.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Masked autoencoders are scalable vision learners

Reference 21

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This paper cites UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed Graphs.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed Graphs

Reference 22

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Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Graphmae: Self-supervised masked graph autoencoders

Reference 23

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This paper cites Strategies for pre-training graph neural networks.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Strategies for pre-training graph neural networks

Reference 24

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This paper cites Pre-training graph neural networks for generic structural feature extraction.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Pre-training graph neural networks for generic structural feature extraction

Reference 25

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This paper cites S., and Leskovec, J.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees S., and Leskovec, J

Reference 26

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This paper cites Self-supervised learning on graphs: Deep insights and new direction.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Self-supervised learning on graphs: Deep insights and new direction

Reference 27

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Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Unresolved cited work

Reference 28

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This paper cites Multi-task self-supervised graph neural networks enable stronger task generalization.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Multi-task self-supervised graph neural networks enable stronger task generalization

Reference 29

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Reference 30

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Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees On the transferability of spectral graph filters

Reference 31

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Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Transferability of spectral graph convolutional neural networks

Reference 32

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Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees X., and Li, J

Reference 33

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Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees One for all: Towards training one graph model for all classification tasks

Reference 34

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Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Graphprompt: Unifying pre-training and downstream tasks for graph neural networks

Reference 35

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Reference 36

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Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Revisiting heterophily for graph neural networks

Reference 37

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Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Hypergraph contrastive learning for drug trafficking community detection

Reference 38

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Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Is homophily a necessity for graph neural networks? In ICLR, 2022

Reference 39

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Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Graph foundation models are already here

Reference 40

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Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees L., Lenssen, J

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Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Wl meet vc

Reference 42

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Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Towards trustworthy retrieval augmented generation for large language models: A survey

Reference 43

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Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Dual-level hypergraph contrastive learning with adaptive temperature enhancement

Reference 44

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Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Adaptive graph enhancement for imbalanced multi-relation graph learning

Reference 45

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Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Gcc: Graph contrastive coding for graph neural network pre-training

Reference 46

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Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees and Gurevych, I

Reference 47

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Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Perturbation bounds for means of eigenvalues and invariant subspaces

Reference 48

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Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Graphon neural networks and the transferability of graph neural networks

Reference 49

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Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees A., Cubuk, E

Reference 50

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Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Preference ranking optimization for human alignment

Reference 51

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Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees and Ribeiro, B

Reference 52

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This paper cites A molecular multimodal foundation model associating molecule graphs with natural language.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees A molecular multimodal foundation model associating molecule graphs with natural language

Reference 53

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Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Gppt: Graph pre-training and prompt tuning to generalize graph neural networks

Reference 54

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

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees All in one: Multi-task prompting for graph neural networks

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:13.415382Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 95969635-6d45-426a-a207-f3d9be42aa6a · outbound

This paper cites Fine-tuning graph neural networks by preserving graph generative patterns.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Fine-tuning graph neural networks by preserving graph generative patterns

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:13.397983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a4678be6-281c-4a5f-b749-803cc0bb1d2a · outbound

This paper cites Transductive linear probing: a novel framework for few-shot node classification.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Transductive linear probing: a novel framework for few-shot node classification

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:13.381992Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c6e402fd-8ee9-4b05-a865-8d5f37590d5a · outbound

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

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Graphgpt: Graph instruction tuning for large language models

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:13.366148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 91e8674d-d1ac-4324-a487-b3ebfbdeef76 · outbound

This paper cites Galactica: A large language model for science.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Galactica: A large language model for science

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:13.347888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8c82ba25-2c45-4e74-8dec-ed3af6c9839f · outbound

This paper cites G., Azabou, M., Dyer, E.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees G., Azabou, M., Dyer, E

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:13.329572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a8118e91-4f1b-430e-8362-dcbbd7763b83 · outbound

This paper cites Llama: Open and efficient foundation language models.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Llama: Open and efficient foundation language models

Reference 61

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Source-reported events for the cited work

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Observation 7110fcec-313f-453e-8082-ec01f54a23a0 · outbound

This paper cites Graph attention networks.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Graph attention networks

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:13.291618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 988c368c-b0ce-405f-91da-a194c2ed9523 · outbound

This paper cites Can language models solve graph problems in natural language? In NeurIPS, 2024 a.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Can language models solve graph problems in natural language? In NeurIPS, 2024 a

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:13.272060Z

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Observation e5724db6-92ac-449a-8e84-cd1e3a712365 · outbound

This paper cites Graph few-shot learning with task-specific structures.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Graph few-shot learning with task-specific structures

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:13.254896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1419eeb8-30b8-4cd9-b50d-3ede451f0bdd · outbound

This paper cites Task-adaptive few-shot node classification.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Task-adaptive few-shot node classification

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:13.237836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 668f0ef7-d7d1-4b30-8ec8-5da57549f9ab · outbound

This paper cites V., Zhang, C., and Ye, Y.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees V., Zhang, C., and Ye, Y

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:13.211593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 423f9ea8-0a09-45a1-b2fd-100c99d2be0a · outbound

This paper cites Subgraph pooling: Tackling negative transfer on graphs.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Subgraph pooling: Tackling negative transfer on graphs

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:13.190510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5027ae82-858b-4b57-814c-f180ebf630e4 · outbound

This paper cites Can llms convert graphs to text-attributed graphs? In NAACL, 2025 a.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Can llms convert graphs to text-attributed graphs? In NAACL, 2025 a

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:13.171826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9e2a23b4-3622-4b68-bebd-ceaa1b201a5f · outbound

This paper cites V., Zhang, C., and Ye, Y.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees V., Zhang, C., and Ye, Y

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:13.153470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation dc79cb94-3e01-4210-bb9b-006f843e9785 · outbound

This paper cites Training mlps on graphs without supervision.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Training mlps on graphs without supervision

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:13.135108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6bfecc74-8f2a-40ad-93c1-4ea56abf533b · outbound

This paper cites Y., Guu, K., Yu, A.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Y., Guu, K., Yu, A

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:12.968540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b2236820-6159-4407-8bb2-21729612da64 · outbound

This paper cites From coarse to fine: enable comprehensive graph self-supervised learning with multi-granular semantic ensemble.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees From coarse to fine: enable comprehensive graph self-supervised learning with multi-granular semantic ensemble

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:12.951963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4155c7b4-8e42-4629-bc33-b8e7e168248e · outbound

This paper cites and Huang, C.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees and Huang, C

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:12.935350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation dfcd3c4c-0d50-4acf-8ea8-1e6600ca3b53 · outbound

This paper cites Opengraph: Towards open graph foundation models.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Opengraph: Towards open graph foundation models

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:12.917261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8840aa6c-5824-4043-a8b8-c7b9c6c2a053 · outbound

This paper cites M., Raghunathan, A., Liang, P., and Ma, T.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees M., Raghunathan, A., Liang, P., and Ma, T

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:12.899327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7deaf86f-b95d-4cf9-8803-ef47f53cca9a · outbound

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

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees How powerful are graph neural networks? In ICLR, 2019

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-11T10:41:12.489922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d53f5bff-ec3b-4a70-91ed-a80b1e098b83 · outbound

This paper cites Text-free multi-domain graph pre-training: Toward graph foundation models.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Text-free multi-domain graph pre-training: Toward graph foundation models

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:12.870988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 73bac78b-93d2-4b0e-ab38-8c218f9d587f · outbound

This paper cites Florence: A new foundation model for computer vision.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Florence: A new foundation model for computer vision

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:12.856257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 81e43eb0-1166-4d3f-bbcc-45edfc28d6e1 · outbound

This paper cites A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:12.841188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 09a0b6af-2c9c-4d1d-8969-eaf1e6cbd5ca · outbound

This paper cites Beyond weisfeiler-lehman: A quantitative framework for gnn expressiveness.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Beyond weisfeiler-lehman: A quantitative framework for gnn expressiveness

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:12.824922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1f5764cf-6340-4b09-9a80-ffc631045015 · outbound

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Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Unresolved cited work

Reference 81

Resolution: unresolved; raw_fallback, observed 2026-08-11T10:41:12.808174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a9bc3320-04cf-42b0-be5a-06f8253e72e2 · outbound

This paper cites Dtgb: A comprehensive benchmark for dynamic text-attributed graphs.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Dtgb: A comprehensive benchmark for dynamic text-attributed graphs

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:12.791722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cdd83184-8977-4e9a-bc50-cfaed915b50e · outbound

This paper cites Labeling trick: A theory of using graph neural networks for multi-node representation learning.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Labeling trick: A theory of using graph neural networks for multi-node representation learning

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:12.774136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f63a1393-d3d5-4163-b245-2468db203917 · outbound

This paper cites V., Zhang, C., and Ye, Y.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees V., Zhang, C., and Ye, Y

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:12.756802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e93d0700-0e93-46ef-8ec9-d653fa9d0564 · outbound

This paper cites Gimlet: A unified graph-text model for instruction-based molecule zero-shot learning.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Gimlet: A unified graph-text model for instruction-based molecule zero-shot learning

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:12.737747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ea27b729-2784-4c0f-9625-6b025f73c246 · outbound

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

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees 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 2026-08-11T10:41:12.722047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 52746894-49cd-4585-9522-940318e98a81 · outbound

This paper cites Graphany: A foundation model for node classification on any graph.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Graphany: A foundation model for node classification on any graph

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:12.704371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3d5a7346-bcbc-49e0-8424-e6b65c403848 · outbound

This paper cites Transfer learning of graph neural networks with ego-graph information maximization.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Transfer learning of graph neural networks with ego-graph information maximization

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:12.687617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b199823a-4a78-416e-b421-0c69abaddc45 · outbound

This paper cites Deep graph contrastive representation learning.

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Deep graph contrastive representation learning

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:41:12.667039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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