Fetching the paper…
Reading the bibliography…
Large language models have evolved to process multiple modalities beyond text, such as images and audio, which motivates us to explore how to effectively leverage them for graph reasoning tasks.
Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. 2019 · 1907
Earlier work this paper cites.
Description of language design
Martin Joos. 1950 · 1950
Earlier work this paper cites.
Distributional structure
Zellig S Harris. 1954 · 1954
Earlier work this paper cites.
A synopsis of linguistic theory, 1930-1955
John R Firth. 1957 · 1955
Earlier work this paper cites.
On random graphs
Paul Erdős and Alfréd Rényi. 1959 · 1959
Earlier work this paper cites.
Centrality in social networks conceptual clarification
Linton C Freeman. 1978 · 1978
Earlier work this paper cites.
Stochastic blockmodels: First steps
Paul W. Holland, Kathryn B. Laskey, and Samuel Leinhardt. 1983 · 1983
Earlier work this paper cites.
Network structure and minimum degree
Stephen B Seidman. 1983 · 1983
Earlier work this paper cites.
Book reviews: Text generation and systemic-functional linguistics: Experiences from English and Japanese
Terry Patten. 1993 · 1993
Earlier work this paper cites.
Gml: Graph modelling language
Michael Himsolt. 1997 · 1997
Earlier work this paper cites.
The anatomy of a large-scale hypertextual web search engine
Sergey Brin and Lawrence Page. 1998 · 1998
Earlier work this paper cites.
Emergence of scaling in random networks
Albert-László Barabási and Réka Albert. 1999 · 1999
Earlier work this paper cites.
The pagerank citation ranking: Bringing order to the web
Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999 · 1999
Earlier work this paper cites.
Statistical mechanics of complex networks
Réka Albert and Albert-László Barabási. 2002 · 2002
Earlier work this paper cites.
Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2020 · 2009
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. 2020 · 2010
Earlier work this paper cites.
A generalization of transformer networks to graphs
Vijay Prakash Dwivedi and Xavier Bresson. 2020 · 2012
Earlier work this paper cites.
Abstract Meaning Representation for sembanking
Laura Banarescu, Claire Bonial, Shu Cai, Madalina Georgescu, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Philipp Koehn, Martha Palmer, and Nathan Schneider. 2013 · 2013
Earlier work this paper cites.
Graph markup language (graphml)
Ulrik Brandes, Markus Eiglsperger, Jürgen Lerner, and Christian Pich. 2013 · 2013
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Predicting multicellular function through multi-layer tissue networks
Marinka Zitnik and Jure Leskovec. 2017 · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
Speech-transformer: a no-recurrence sequence-to-sequence model for speech recognition
Linhao Dong, Shuang Xu, and Bo Xu. 2018 · 2018
Cited alongside, same era.
Beyond text: A deep dive into large language models’ ability on understanding graph data
Yuntong Hu, Zheng Zhang, and Liang Zhao. 2023 · 2023
Later among the works it cites.
Evaluating large language models on graphs: Performance insights and comparative analysis
Chang Liu and Bo Wu. 2023 · 2023
Later among the works it cites.
Multimodal learning with transformers: A survey
Peng Xu, Xiatian Zhu, and David A Clifton. 2023 · 2023
Later among the works it cites.
Natural language is all a graph needs
Ruosong Ye, Caiqi Zhang, Runhui Wang, Shuyuan Xu, and Yongfeng Zhang. 2023 · 2023
Later among the works it cites.
A survey on multimodal large language models
Shukang Yin, Chaoyou Fu, Sirui Zhao, Ke Li, Xing Sun, Tong Xu, and Enhong Chen. 2023 · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Learning structural node embeddings via diffusion wavelets
Claire Donnat, Marinka Zitnik, David Hallac, and Jure Leskovec. 2018 · 2018
Cited alongside, same era.
Benchmarking graph neural networks
Zhilin Yang, William W Cohen, and Ruslan Salakhutdinov. 2018 · 2018
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
Cited alongside, same era.
Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020 · 2020
Cited alongside, same era.
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 · 2020
Cited alongside, same era.
Promoting graph awareness in linearized graph-to-text generation
Alexander Miserlis Hoyle, Ana Marasović, and Noah A. Smith. 2021 · 2021
Cited alongside, same era.
Later among the works it cites.
Graph meets llms: Towards large graph models
Ziwei Zhang, Haoyang Li, Zeyang Zhang, Yijian Qin, Xin Wang, and Wenwu Zhu. 2023b · 2023
Later among the works it cites.
Graphtext: Graph reasoning in text space
Jianan Zhao, Le Zhuo, Yikang Shen, Meng Qu, Kai Liu, Michael Bronstein, Zhaocheng Zhu, and Jian Tang. 2023 · 2023
Later among the works it cites.
GroundHog: Dialogue generation using multi-grained linguistic input
Alexander Chernyavskiy, Lidiia Ostyakova, and Dmitry Ilvovsky. 2024 · 2024
Closest in time.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024 · 2024
Closest in time.
Can llms effectively leverage graph structural information through prompts, and why?
Jin Huang, Xingjian Zhang, Qiaozhu Mei, and Jiaqi Ma. 2024 · 2024
Closest in time.
Graphinstruct: Empowering large language models with graph understanding and reasoning capability
Zihan Luo, Xiran Song, Hong Huang, Jianxun Lian, Chenhao Zhang, Jinqi Jiang, Xing Xie, and Hai Jin. 2024 · 2024
Closest in time.
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 · 2024
Closest in time.
Leveraging discourse structure for extractive meeting summarization
Virgile Rennard, Guokan Shang, Michalis Vazirgiannis, and Julie Hunter. 2024 · 2024
Closest in time.
Graph reasoning with large language models via pseudo-code prompting
Konstantinos Skianis, Giannis Nikolentzos, and Michalis Vazirgiannis. 2024 · 2024
Closest in time.
Can language models solve graph problems in natural language?
Heng Wang, Shangbin Feng, Tianxing He, Zhaoxuan Tan, Xiaochuang Han, and Yulia Tsvetkov. 2024 · 2024
Closest in time.
Grapheval2000: Benchmarking and improving large language models on graph datasets
Qiming Wu, Zichen Chen, Will Corcoran, Misha Sra, and Ambuj K Singh. 2024 · 2024
Closest in time.
Exploring the potential of large language models in graph generation
Yang Yao, Xin Wang, Zeyang Zhang, Yijian Qin, Ziwei Zhang, Xu Chu, Yuekui Yang, Wenwu Zhu, and Hong Mei. 2024 · 2024
Closest in time.
Gracore: Benchmarking graph comprehension and complex reasoning in large language models
Zike Yuan, Ming Liu, Hui Wang, and Bing Qin. 2024 · 2024
Closest in time.
An Yang, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoyan Huang, Jiandong Jiang, Jianhong Tu, Jianwei Zhang, Jingren Zhou, et al. 2025 · 2025
Closest in time.