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Node classification is a fundamental task in graph analysis, with broad applications across various fields.
Term-weighting approaches in automatic text retrieval
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Citeseer: an automatic citation indexing system
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Semi-supervised learning using gaussian fields and harmonic functions
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Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
Belkin, M., Niyogi, P., and Sindhwani, V · 2006
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Collective classification in network data
Sen, P., Namata, G., Bilgic, M., Getoor, L., Gallagher, B., and Eliassi-Rad, T · 2008
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Node2vec: Scalable feature learning for networks
Grover, A. and Leskovec, J · 2016
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Revisiting semi-supervised learning with graph embeddings
Yang, Z., Cohen, W., and Salakhudinov, R · 2016
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Inductive representation learning on large graphs
Hamilton, W. L., Ying, R., and Leskovec, J · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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Graph attention networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
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Roberta: A robustly optimized bert pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 2019
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Justifying recommendations using distantly-labeled reviews and fine-grained aspects
Ni, J., Li, J., and McAuley, J · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
Reimers, N. and Gurevych, I · 2019
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Simplifying graph convolutional networks
Wu, F., Zhang, T., de Souza, A. H., Fifty, C., Yu, T., and Weinberger, K. Q · 2019
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How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
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Open graph benchmark: Datasets for machine learning on graphs
Hu, W., Fey, M., Zitnik, M., Dong, Y., Ren, H., Liu, B., Catasta, M., and Leskovec, J · 2020
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Item tagging for information retrieval: A tripartite graph neural network based approach
Mao, K., Xiao, X., Zhu, J., Lu, B., Tang, R., and He, X · 2020
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Wiki-cs: A wikipedia-based benchmark for graph neural networks
Mernyei, P. and Cangea, C · 2020
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Beyond homophily in graph neural networks: Current limitations and effective designs
Zhu, J., Yan, Y., Zhao, L., Heimann, M., Akoglu, L., and Koutra, D · 2020
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Deep Learning on Graphs
Ma, Y. and Tang, J · 2021
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Masked label prediction: Unified massage passing model for semi-supervised classification
Shi, Y., Huang, Z., Wang, W., Zhong, H., Feng, S., and Sun, Y · 2021
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Relation-aware heterogeneous graph for user profiling
Yan, Q., Zhang, Y., Liu, Q., Wu, S., and Wang, L · 2021
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LoRA: Low-rank adaptation of large language models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2022
Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., and et al, A. F · 2024
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Gaugllm: Improving graph contrastive learning for text-attributed graphs with large language models
Fang, Y., Fan, D., Zha, D., and Tan, Q · 2024
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Harnessing explanations: LLM-to-LM interpreter for enhanced text-attributed graph representation learning
He, X., Bresson, X., Laurent, T., Perold, A., LeCun, Y., and Hooi, B · 2024
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Can gnn be good adapter for llms?
Huang, X., Han, K., Yang, Y., Bao, D., Tao, Q., Chai, Z., and Zhu, Q · 2024
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Nt-llm: A novel node tokenizer for integrating graph structure into large language models
Ji, Y., Liu, C., Chen, X., Ding, Y., Luo, D., Li, M., Lin, W., and Lu, H · 2024
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Text embeddings by weakly-supervised contrastive pre-training
Wang, L., Yang, N., Huang, X., Jiao, B., Yang, L., Jiang, D., Majumder, R., and Wei, F · 2022
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Chain of thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E. H., Xia, F., Le, Q., and Zhou, D · 2022
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GraphLLM: Boosting graph reasoning ability of large language model
Chai, Z., Zhang, T., Wu, L., Han, K., Hu, X., Huang, X., and Yang, Y · 2023
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Can llms effectively leverage graph structural information through prompts, and why?
Huang, J., Zhang, X., Mei, Q., and Ma, J · 2023
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Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., de Las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L. R., Lachaux, M.-A., Stock, P., Scao, T. L., Lavril, T., Wang, T., Lacroix, T., and Sayed, W. E · 2023
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Patton: Language model pretraining on text-rich networks
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One for all: Towards training one graph model for all classification tasks
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Gpt-4 technical report, 2024
OpenAI · 2024
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Let your graph do the talking: Encoding structured data for llms, 2024
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Can graph learning improve planning in llm-based agents?
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Llm4dyg: Can large language models solve spatial-temporal problems on dynamic graphs?
Zhang, Z., Wang, X., Zhang, Z., Li, H., Qin, Y., and Zhu, W · 2024
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Efficient tuning and inference for large language models on textual graphs
Zhu, Y., Wang, Y., Shi, H., and Tang, S · 2024
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How do large language models understand graph patterns? a benchmark for graph pattern comprehension
Dai, X., Qu, H., Shen, Y., Zhang, B., Wen, Q., Fan, W., Li, D., Tang, J., and Shan, C · 2025
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Gofa: A generative one-for-all model for joint graph language modeling
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Qwen2.5 technical report, 2025
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Grapharena: Benchmarking large language models on graph computational problems
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Model generalization on text attribute graphs: Principles with large language models, 2025
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Graphclip: Enhancing transferability in graph foundation models for text-attributed graphs
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