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Although Large Language Models (LLMs) have demonstrated remarkable progress, their proficiency in graph-related tasks remains notably limited, hindering the development of truly general-purpose models.
What can neural networks reason about?
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The network data repository with interactive graph analytics and visualization
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Semi-supervised classification with graph convolutional networks
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Inductive representation learning on large graphs
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Mastering the game of go without human knowledge
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How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2018
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Link prediction based on graph neural networks
Zhang, M. and Chen, Y · 2018
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Knowledge graph embedding based question answering
Huang, X., Zhang, J., Li, D., and Li, P · 2019
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Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
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Erdos goes neural: an unsupervised learning framework for combinatorial optimization on graphs
Karalias, N. and Loukas, A · 2020
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Neural execution of graph algorithms
Veličković, P., Ying, R., Padovano, M., Hadsell, R., and Blundell, C · 2020
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Gcn-rl circuit designer: Transferable transistor sizing with graph neural networks and reinforcement learning
Wang, H., Wang, K., Yang, J., Shen, L., Sun, N., Lee, H.-S., and Han, S · 2020
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Measuring mathematical problem solving with the math dataset
Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., and Steinhardt, J · 2021
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A graph placement methodology for fast chip design
Mirhoseini, A., Goldie, A., Yazgan, M., Jiang, J. W., Songhori, E., Wang, S., Lee, Y.-J., Johnson, E., Pathak, O., Nova, A., et al · 2021
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Chain-of-thought prompting elicits reasoning in large language models
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Talk like a graph: Encoding graphs for large language models
Fatemi, B., Halcrow, J., and Perozzi, B · 2023
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Efficient memory management for large language model serving with pagedattention
Kwon, W., Li, Z., Zhuang, S., Sheng, Y., Zheng, L., Yu, C. H., Gonzalez, J. E., Zhang, H., and Stoica, I · 2023
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Understanding transformer reasoning capabilities via graph algorithms
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Deepseekmath: Pushing the limits of mathematical reasoning in open language models
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Qwen2.5: A party of foundation models, September 2024
Team, Q · 2024
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Grapheval2000: Benchmarking and improving large language models on graph datasets
Wu, Q., Chen, Z., Corcoran, W., Sra, M., and Singh, A. K · 2024
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Qwen2.5-math technical report: Toward mathematical expert model via self-improvement
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Can language models solve graph problems in natural language?
Wang, H., Feng, S., He, T., Tan, Z., Han, X., and Tsvetkov, Y · 2023
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Scaling relationship on learning mathematical reasoning with large language models
Yuan, Z., Yuan, H., Li, C., Dong, G., Lu, K., Tan, C., Zhou, C., and Zhou, J · 2023
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Llama3 foundation models
AI, M · 2024
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Graphwiz: An instruction-following language model for graph computational problems
Chen, N., Li, Y., Tang, J., and Li, J · 2024
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Which modality should i use–text, motif, or image?: Understanding graphs with large language models
Das, D., Gupta, I., Srivastava, J., and Kang, D · 2024
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Ruler: What’s the real context size of your long-context language models?
Hsieh, C.-P., Sun, S., Kriman, S., Acharya, S., Rekesh, D., Jia, F., Zhang, Y., and Ginsburg, B · 2024
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Can large language models analyze graphs like professionals? a benchmark, datasets and models
Li, X., Chen, W., Chu, Q., Li, H., Sun, Z., Li, R., Qian, C., Wei, Y., Shi, C., Liu, Z., et al · 2024
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Language is all a graph needs
Ye, R., Zhang, C., Wang, R., Xu, S., and Zhang, Y · 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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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., Zhu, Q., Ma, S., Wang, P., Bi, X., et al · 2025
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Gofa: A generative one-for-all model for joint graph language modeling
Kong, L., Feng, J., Liu, H., Huang, C., Huang, J., Chen, Y., and Zhang, M · 2025
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Li, Y., Pan, Z., Lin, H., Sun, M., He, C., and Wu, L · 2025
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Qwen2.5 technical report, 2025
Qwen, :, Yang, A., Yang, B., Zhang, B., Hui, B., Zheng, B., Yu, B., Li, C., Liu, D., Huang, F., Wei, H., Lin, H., Yang, J., Tu, J., Zhang, J., Yang, J., Yang, J., Zhou, J., Lin, J., Dang, K., Lu, K., Bao, K., Yang, K., Yu, L., Li, M., Xue, M., Zhang, P., Zhu, Q., Men, R., Lin, R., Li, T., Tang, T., Xia, T., Ren, X., Ren, X., Fan, Y., Su, Y., Zhang, Y., Wan, Y., Liu, Y., Cui, Z., Zhang, Z., and Qiu, Z · 2025
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Grapharena: Benchmarking large language models on graph computational problems
Tang, J., Zhang, Q., Li, Y., and Li, J · 2025
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Exploring graph tasks with pure llms: A comprehensive benchmark and investigation
Wang, Y., Dai, X., Fan, W., and Ma, Y · 2025
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When more is less: Understanding chain-of-thought length in llms
Wu, Y., Wang, Y., Du, T., Jegelka, S., and Wang, Y · 2025
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Xu, H., Jian, X., Zhao, X., Pang, W., Zhang, C., Wang, S., Zhang, Q., Monteiro, J., Sun, Q., and Yu, T · 2025
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Gracore: Benchmarking graph comprehension and complex reasoning in large language models
Yuan, Z., Liu, M., Wang, H., and Qin, B · 2025
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