S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs | Paper Citation Record

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This paper cites Invariant Risk Minimization.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Invariant Risk Minimization

Reference 1

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This paper cites ConGraT: Self-supervised contrastive pretraining for joint graph and text embeddings.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs ConGraT: Self-supervised contrastive pretraining for joint graph and text embeddings

Reference 2

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This paper cites Multi-level graph convolutional networks for cross-platform anchor link prediction.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Multi-level graph convolutional networks for cross-platform anchor link prediction

Reference 3

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This paper cites Brainnet: Epileptic wave detection from seeg with hierarchical graph diffusion learning.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Brainnet: Epileptic wave detection from seeg with hierarchical graph diffusion learning

Reference 4

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This paper cites LLaGA: Large Language and Graph Assistant.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs LLaGA: Large Language and Graph Assistant

Reference 5

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This paper cites Text-space graph foundation models: Comprehensive benchmarks and new insights.Advances in Neural Information Processing Systems, 37:7464– 7492.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Text-space graph foundation models: Comprehensive benchmarks and new insights.Advances in Neural Information Processing Systems, 37:7464– 7492

Reference 6

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

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs TAGLAS: An atlas of text-attributed graph datasets in the era of large graph and language models

Reference 7

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This paper cites Domain-adversarial training of neural networks.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Domain-adversarial training of neural networks

Reference 8

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

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning

Reference 9

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

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Graphmae: Self-supervised masked graph autoencoders

Reference 10

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Observation 13055ab8-5589-470b-a6b2-80ad3a4ea53d · outbound

This paper cites Open graph benchmark: Datasets for machine learning on graphs.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Open graph benchmark: Datasets for machine learning on graphs

Reference 11

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This paper cites Can gnn be good adapter for llms? InProceedings of the ACM web conference 2024, pages 893–904.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Can gnn be good adapter for llms? InProceedings of the ACM web conference 2024, pages 893–904

Reference 12

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This paper cites Out-of-distribution generalization via risk ex- trapolation (rex).

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Out-of-distribution generalization via risk ex- trapolation (rex)

Reference 13

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This paper cites Learning invariant graph representations for out-of-distribution generalization.Advances in Neural Information Processing Systems, 35:11828–11841.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Learning invariant graph representations for out-of-distribution generalization.Advances in Neural Information Processing Systems, 35:11828–11841

Reference 14

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This paper cites GRENADE: Graph-centric language model for self-supervised representation learning on text-attributed graphs.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs GRENADE: Graph-centric language model for self-supervised representation learning on text-attributed graphs

Reference 15

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Observation 2b823fed-e7ee-4ef1-a55b-04e51af3f3c7 · outbound

This paper cites Zerog: Investigating cross- dataset zero-shot transferability in graphs.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Zerog: Investigating cross- dataset zero-shot transferability in graphs

Reference 16

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

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs One for All: Towards Training One Graph Model for All Classification Tasks

Reference 17

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

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Graph Foundation Models: Concepts, Opportunities and Challenges

Reference 18

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This paper cites Learning noise-resilient and transferable graph-text alignment via dynamic quality assessment.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Learning noise-resilient and transferable graph-text alignment via dynamic quality assessment

Reference 19

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This paper cites Learning transferable features with deep adaptation networks.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Learning transferable features with deep adaptation networks

Reference 20

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This paper cites Decoupled Weight Decay Regularization.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Decoupled Weight Decay Regularization

Reference 21

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This paper cites Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks

Reference 22

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S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Unresolved cited work

Reference 23

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This paper cites Learning transferable visual models from natural language supervision.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Learning transferable visual models from natural language supervision

Reference 24

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This paper cites Recipe for a general, powerful, scalable graph transformer.Advances in Neural Information Processing Systems, 35:14501–14515.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Recipe for a general, powerful, scalable graph transformer.Advances in Neural Information Processing Systems, 35:14501–14515

Reference 25

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This paper cites Sentence-bert: Sentence embeddings using siamese bert- networks.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Sentence-bert: Sentence embeddings using siamese bert- networks

Reference 26

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This paper cites A survey of large lan- guage models for graphs.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs A survey of large lan- guage models for graphs

Reference 27

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This paper cites Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization

Reference 28

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This paper cites Collective classification in network data.AI magazine, 29(3):93–93.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Collective classification in network data.AI magazine, 29(3):93–93

Reference 29

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This paper cites Deep coral: Correlation alignment for deep domain adaptation.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Deep coral: Correlation alignment for deep domain adaptation

Reference 30

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

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Graphgpt: Graph instruction tuning for large language models

Reference 31

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This paper cites Bootstrapped representation learning on graphs.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Bootstrapped representation learning on graphs

Reference 32

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This paper cites Visualizing data using t-sne.Journal of Machine Learning Research, 9(86):2579–2605.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Visualizing data using t-sne.Journal of Machine Learning Research, 9(86):2579–2605

Reference 33

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This paper cites Deep graph infomax.stat, 1050:21.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Deep graph infomax.stat, 1050:21

Reference 34

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This paper cites Text Embeddings by Weakly-Supervised Contrastive Pre-training.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Text Embeddings by Weakly-Supervised Contrastive Pre-training

Reference 35

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This paper cites Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers.Advances in neural information processing systems, 33:5776–5788.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers.Advances in neural information processing systems, 33:5776–5788

Reference 36

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Observation 9ca140a1-de69-45fd-8e91-966a7960c74d · outbound

This paper cites Augmenting low-resource text classification with graph-grounded pre-training and prompting.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Augmenting low-resource text classification with graph-grounded pre-training and prompting

Reference 37

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This paper cites Weighted Risk Invariance: Domain Generalization under Invariant Feature Shift.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Weighted Risk Invariance: Domain Generalization under Invariant Feature Shift

Reference 38

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This paper cites Handling Distribution Shifts on Graphs: An Invariance Perspective.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Handling Distribution Shifts on Graphs: An Invariance Perspective

Reference 39

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This paper cites A comprehensive study on text-attributed graphs: Benchmarking and rethinking.Advances in Neural Information Processing Systems, 36:17238– 17264.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs A comprehensive study on text-attributed graphs: Benchmarking and rethinking.Advances in Neural Information Processing Systems, 36:17238– 17264

Reference 40

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This paper cites Qwen3 Technical Report.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Qwen3 Technical Report

Reference 41

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This paper cites Deep Graph Contrastive Representation Learning.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Deep Graph Contrastive Representation Learning

Reference 42

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This paper cites Graphclip: Enhancing transferability in graph foundation models for text-attributed graphs.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Graphclip: Enhancing transferability in graph foundation models for text-attributed graphs

Reference 43

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S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Unresolved cited work

Reference 44

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S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Unresolved cited work

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S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Unresolved cited work

Reference 46

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This paper cites Proposition 1If the assumptions hold and the predictor f depends only on Z, then the weighted risks are equal across all domains.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Proposition 1If the assumptions hold and the predictor f depends only on Z, then the weighted risks are equal across all domains

Reference 47

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This paper cites an unresolved cited work.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Unresolved cited work

Reference 48

Resolution: unresolved; raw_fallback, observed 2026-05-21T08:14:52.627394Z

Source-reported events for the cited work

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Observation 78fbbb47-fecd-4db5-8710-89c2525b6569 · outbound

This paper cites an unresolved cited work.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-05-21T08:14:52.647219Z

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Observation 5f7de84f-b31d-4ce5-98bb-9b52b8f9dae4 · outbound

This paper cites an unresolved cited work.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-05-21T08:14:52.666763Z

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Observation 901bbd44-b909-48f3-a2ee-e516aef0b54f · outbound

This paper cites • GraphCLIP[ 43]: This framework relies heavily on contrastive alignment objectives.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs • GraphCLIP[ 43]: This framework relies heavily on contrastive alignment objectives

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T08:14:52.671544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Pith citing papers