Fetching the paper…
Reading the bibliography…
Large Language Models (LLMs) are increasingly used for various tasks with graph structures.
Solution of a large-scale traveling-salesman problem
George Dantzig, Ray Fulkerson, and Selmer Johnson · 1954
Earlier work this paper cites.
Maximal flow through a network
Lester Randolph Ford and Delbert R Fulkerson · 1956
Earlier work this paper cites.
A note on two problems in connexion with graphs
Edsger W Dijkstra · 1959
Earlier work this paper cites.
Topological sorting of large networks
Arthur B Kahn · 1962
Earlier work this paper cites.
An optimal algorithm for on-line bipartite matching
Richard M Karp, Umesh V Vazirani, and Vijay V Vazirani · 1990
Earlier work this paper cites.
An open graph visualization system and its applications to software engineering
Emden R Gansner and Stephen C North · 2000
Earlier work this paper cites.
Lethality and centrality in protein networks
Hawoong Jeong, Sean P Mason, A-L Barabási, and Zoltan N Oltvai · 2001
Earlier work this paper cites.
Algorithms in C, part 5: graph algorithms
Robert Sedgewick · 2001
Earlier work this paper cites.
Advances on the hamiltonian problem–a survey
Ronald J Gould · 2003
Earlier work this paper cites.
The structure and function of complex networks
Mark EJ Newman · 2003
Earlier work this paper cites.
The political blogosphere and the 2004 us election: divided they blog
Lada A Adamic and Natalie Glance · 2005
Earlier work this paper cites.
Proteome survey reveals modularity of the yeast cell machinery
Anne-Claude Gavin, Patrick Aloy, Paola Grandi, Roland Krause, Markus Boesche, Martina Marzioch, Christina Rau, Lars Juhl Jensen, Sonja Bastuck, Birgit Dümpelfeld, et al · 2006
Earlier work this paper cites.
Graph evolution: Densification and shrinking diameters
Jure Leskovec, Jon Kleinberg, and Christos Faloutsos · 2007
Earlier work this paper cites.
Exploring network structure, dynamics, and function using networkx
Aric Hagberg, Pieter Swart, and Daniel S Chult · 2008
Earlier work this paper cites.
Microscopic evolution of social networks
Jure Leskovec, Lars Backstrom, Ravi Kumar, and Andrew Tomkins · 2008
Earlier work this paper cites.
Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky · 2009
Earlier work this paper cites.
Matplotlib for Python developers
Sandro Tosi · 2009
Earlier work this paper cites.
Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko · 2013
Earlier work this paper cites.
Reasoning with neural tensor networks for knowledge base completion
Richard Socher, Danqi Chen, Christopher D Manning, and Andrew Ng · 2013
Earlier work this paper cites.
Neural module networks
Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein · 2016
Earlier work this paper cites.
Interaction networks for learning about objects, relations and physics
Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, et al · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Earlier work this paper cites.
Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William Cohen, and Ruslan Salakhudinov · 2016
Earlier work this paper cites.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Cited alongside, same era.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Cited alongside, same era.
Neural collaborative filtering
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua · 2017
Cited alongside, same era.
Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens Van Der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick · 2017
Cited alongside, same era.
Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio, et al · 2017
Cited alongside, same era.
Relational inductive biases, deep learning, and graph networks
Exploring the potential of large language models (LLMs) in learning on graphs
Zhikai Chen, Haitao Mao, Hang Li, Wei Jin, Hongzhi Wen, Xiaochi Wei, Shuaiqiang Wang, Dawei Yin, Wenqi Fan, Hui Liu, et al · 2023
Later among the works it cites.
Instructblip: Towards general-purpose vision-language models with instruction tuning
W Dai, J Li, D Li, AMH Tiong, J Zhao, W Wang, B Li, P Fung, and S Hoi · 2023
Later among the works it cites.
Jiayan Guo, Lun Du, and Hengyu Liu · 2023
Later among the works it cites.
Effective structured-prompting by meta-learning and representitive verbalizer
Weisen Jiang, Yu Zhang, and James Kwok · 2023
Later among the works it cites.
BLIP-2: bootstrapping language-image pre-training with frozen image encoders and large language models
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
Cited alongside, same era.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
Cited alongside, same era.
Gqa: A new dataset for real-world visual reasoning and compositional question answering
Drew A Hudson and Christopher D Manning · 2019
Cited alongside, same era.
A corpus for reasoning about natural language grounded in photographs
Alane Suhr, Stephanie Zhou, Ally Zhang, Iris Zhang, Huajun Bai, and Yoav Artzi · 2019
Cited alongside, same era.
From recognition to cognition: Visual commonsense reasoning
Rowan Zellers, Yonatan Bisk, Ali Farhadi, and Yejin Choi · 2019
Cited alongside, same era.
Raven: A dataset for relational and analogical visual reasoning
Chi Zhang, Feng Gao, Baoxiong Jia, Yixin Zhu, and Song-Chun Zhu · 2019
Cited alongside, same era.
Popularity prediction on social platforms with coupled graph neural networks
Qi Cao, Huawei Shen, Jinhua Gao, Bingzheng Wei, and Xueqi Cheng · 2020
Cited alongside, same era.
Later among the works it cites.
Evaluating large language models on graphs: Performance insights and comparative analysis
Chang Liu and Bo Wu · 2023
Later among the works it cites.
Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 2023
Later among the works it cites.
GPT-4 Turbo
OpenAI · 2023
Later among the works it cites.
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 · 2023
Later among the works it cites.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
Later among the works it cites.
Can language models solve graph problems in natural language?
Heng Wang, Shangbin Feng, Tianxing He, Zhaoxuan Tan, Xiaochuang Han, and Yulia Tsvetkov · 2023
Later among the works it cites.
Kicgpt: Large language model with knowledge in context for knowledge graph completion
Yanbin Wei, Qiushi Huang, Yu Zhang, and James Kwok · 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
Later among the works it cites.
Judging LLM-as-a-judge with MT-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica · 2023
Later among the works it cites.
Least-to-most prompting enables complex reasoning in large language models
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V Le, and Ed H. Chi · 2023
Later among the works it cites.
Minigpt-4: Enhancing vision-language understanding with advanced large language models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny · 2023
Later among the works it cites.
Llaga: Large language and graph assistant
Runjin Chen, Tong Zhao, Ajay Jaiswal, Neil Shah, and Zhangyang Wang · 2024
Closest in time.
Talk like a graph: Encoding graphs for large language models
Bahare Fatemi, Jonathan Halcrow, and Bryan Perozzi · 2024
Closest in time.
Nemesis: Normalizing the soft-prompt vectors of vision-language models
Shuai Fu, Xiequn Wang, Qiushi Huang, and Yu Zhang · 2024
Closest in time.
Harnessing explanations: LLM-to-LM interpreter for enhanced text-attributed graph representation learning
Xiaoxin He, Xavier Bresson, Thomas Laurent, Adam Perold, Yann LeCun, and Bryan Hooi · 2024
Closest in time.
Forward-backward reasoning in large language models for mathematical verification
Weisen Jiang, Han Shi, Longhui Yu, Zhengying Liu, Yu Zhang, Zhenguo Li, and James Kwok · 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
Closest in time.
MetaMath: Bootstrap your own mathematical questions for large language models
Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T Kwok, Zhenguo Li, Adrian Weller, and Weiyang Liu · 2024
Closest in time.
Mm-llms: Recent advances in multimodal large language models
Duzhen Zhang, Yahan Yu, Chenxing Li, Jiahua Dong, Dan Su, Chenhui Chu, and Dong Yu · 2024
Closest in time.