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This paper introduces GraphOmni, a comprehensive benchmark designed to evaluate the reasoning capabilities of LLMs on graph-theoretic tasks articulated in natural language.
Gml: Graph modelling language
Michael Himsolt · 1997
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Networks, crowds, and markets: Reasoning about a highly connected world , volume 1
David Easley, Jon Kleinberg, et al · 2010
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Graph markup language (graphml)
Ulrik Brandes, Markus Eiglsperger, Jürgen Lerner, and Christian Pich · 2013
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen · 2018
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2020
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S Yu · 2020
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Utilizing graph machine learning within drug discovery and development
Thomas Gaudelet, Ben Day, Arian R Jamasb, Jyothish Soman, Cristian Regep, Gertrude Liu, Jeremy BR Hayter, Richard Vickers, Charles Roberts, Jian Tang, et al · 2021
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A comprehensive survey on community detection with deep learning
Xing Su, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Di Jin, et al · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Graph neural networks in recommender systems: a survey
Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, and Bin Cui · 2022
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Graphllm: Boosting graph reasoning ability of large language model
Ziwei Chai, Tianjie Zhang, Liang Wu, Kaiqiao Han, Xiaohai Hu, Xuanwen Huang, and Yang Yang · 2023
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Beyond text: A deep dive into large language models’ ability on understanding graph data
Yuntong Hu, Zheng Zhang, and Liang Zhao · 2023
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A survey of graph meets large language model: Progress and future directions
Yuhan Li, Zhixun Li, Peisong Wang, Jia Li, Xiangguo Sun, Hong Cheng, and Jeffrey Xu Yu · 2023
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Melanie Sclar, Yejin Choi, Yulia Tsvetkov, and Alane Suhr · 2023
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Walklm: A uniform language model fine-tuning framework for attributed graph embedding
Yanchao Tan, Zihao Zhou, Hang Lv, Weiming Liu, and Carl Yang · 2023
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Can language models solve graph problems in natural language?
Can large language models analyze graphs like professionals? a benchmark, datasets and models
Xin Li, Weize Chen, Qizhi Chu, Haopeng Li, Zhaojun Sun, Ran Li, Chen Qian, Yiwei Wei, Zhiyuan Liu, Chuan Shi, Maosong Sun, and Cheng Yang · 2024
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Position: Graph foundation models are already here
Haitao Mao, Zhikai Chen, Wenzhuo Tang, Jianan Zhao, Yao Ma, Tong Zhao, and Neil Shah · 2024
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Let your graph do the talking: Encoding structured data for llms
Bryan Perozzi, Bahare Fatemi, Dustin Zelle, Anton Tsitsulin, Mehran Kazemi, Rami Al-Rfou, and Jonathan Halcrow · 2024
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Understanding transformer reasoning capabilities via graph algorithms
Clayton Sanford, Bahare Fatemi, Ethan Hall, Anton Tsitsulin, Mehran Kazemi, Jonathan Halcrow, Bryan Perozzi, and Vahab Mirrokni · 2024
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Quantifying language models’ sensitivity to spurious features in prompt design
Melanie Sclar, Yejin Choi, Yulia Tsvetkov, and Alane Suhr · 2024
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Heng Wang, Shangbin Feng, Tianxing He, Zhaoxuan Tan, Xiaochuang Han, and Yulia Tsvetkov · 2023
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A comprehensive study on text-attributed graphs: Benchmarking and rethinking
Hao Yan, Chaozhuo Li, Ruosong Long, Chao Yan, Jianan Zhao, Wenwen Zhuang, Jun Yin, Peiyan Zhang, Weihao Han, Hao Sun, et al · 2023
Cited alongside, same era.
Graphtext: Graph reasoning in text space
Jianan Zhao, Le Zhuo, Yikang Shen, Meng Qu, Kai Liu, Michael Bronstein, Zhaocheng Zhu, and Jian Tang · 2023
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Graphwiz: An instruction-following language model for graph computational problems
Nuo Chen, Yuhan Li, Jianheng Tang, and Jia Li · 2024
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How do large language models understand graph patterns? a benchmark for graph pattern comprehension
Xinnan Dai, Haohao Qu, Yifen Shen, Bohang Zhang, Qihao Wen, Wenqi Fan, Dongsheng Li, Jiliang Tang, and Caihua Shan · 2024
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Debarati Das, Ishaan Gupta, Jaideep Srivastava, and Dongyeop Kang · 2024
Cited alongside, same era.
Talk like a graph: Encoding graphs for large language models
Bahare Fatemi, Jonathan Halcrow, and Bryan Perozzi · 2024
Cited alongside, same era.
G-retriever: Retrieval-augmented generation for textual graph understanding and question answering
Xiaoxin He, Yijun Tian, Yifei Sun, Nitesh Chawla, Thomas Laurent, Yann LeCun, Xavier Bresson, and Bryan Hooi · 2024
Cited alongside, same era.
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Efficient prompt optimization through the lens of best arm identification
Chengshuai Shi, Kun Yang, Zihan Chen, Jundong Li, Jing Yang, and Cong Shen · 2024
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Musegraph: Graph-oriented instruction tuning of large language models for generic graph mining
Yanchao Tan, Hang Lv, Xinyi Huang, Jiawei Zhang, Shiping Wang, and Carl Yang · 2024
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Graphgpt: Graph instruction tuning for large language models
Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Lixin Su, Suqi Cheng, Dawei Yin, and Chao Huang · 2024
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Are large-language models graph algorithmic reasoners?, 2024
Alexander K. Taylor, Anthony Cuturrufo, Vishal Yathish, Mingyu Derek Ma, and Wei Wang · 2024
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Opengraph: Towards open graph foundation models
Lianghao Xia, Ben Kao, and Chao Huang · 2024
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Graphfm: A comprehensive benchmark for graph foundation model
Yuhao Xu, Xinqi Liu, Keyu Duan, Yi Fang, Yu-Neng Chuang, Daochen Zha, and Qiaoyu Tan · 2024
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Graph linearization methods for reasoning on graphs with large language models
Christos Xypolopoulos, Guokan Shang, Xiao Fei, Giannis Nikolentzos, Hadi Abdine, Iakovos Evdaimon, Michail Chatzianastasis, Giorgos Stamou, and Michalis Vazirgiannis · 2024
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Can llm graph reasoning generalize beyond pattern memorization?
Yizhuo Zhang, Heng Wang, Shangbin Feng, Zhaoxuan Tan, Xiaochuang Han, Tianxing He, and Yulia Tsvetkov · 2024
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Pairbench: A systematic framework for selecting reliable judge vlms
Aarash Feizi, Sai Rajeswar, Adriana Romero-Soriano, Reihaneh Rabbany, Spandana Gella, Valentina Zantedeschi, and João Monteiro · 2025
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