Built on

M. Himsolt, “Gml: Graph modelling language,” University of Passau , 1997
1997
Earlier work this paper cites.
G. F. Lawler and V. Limic, Random walk: a modern introduction . Cambridge University Press, 2010, vol. 123
2010
Earlier work this paper cites.
U. Brandes, M. Eiglsperger, J. Lerner, and C. Pich, “Graph markup language (graphml),” 2013
2013
Earlier work this paper cites.
A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining , 2016, pp. 855–864
2016
Earlier work this paper cites.
W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
N. Tandon, B. D. Mishra, K. Sakaguchi, A. Bosselut, and P. Clark, “Wiqa: A dataset for” what if…” reasoning over procedural text,” arXiv preprint arXiv:1909.04739 , 2019
Original
2019
Earlier work this paper cites.
X. Bresson and T. Laurent, “A two-step graph convolutional decoder for molecule generation,” arXiv preprint arXiv:1906.03412 , 2019
Original
2019
Earlier work this paper cites.
L. Yang, Z. Liu, Y. Dou, J. Ma, and P. S. Yu, “Consisrec: Enhancing gnn for social recommendation via consistent neighbor aggregation,” in Proceedings of the 44th international ACM SIGIR conference on Research and development in information retrieval , 2021, pp. 2141–2145
2021
Earlier work this paper cites.

Similar

S. Wang, K. Zhang, L. Wu, H. Ma, R. Hong, and M. Wang, “Privileged graph distillation for cold start recommendation,” in Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2021, pp. 1187–1196
2021
Cited alongside, same era.
Z. Guo, C. Zhang, W. Yu, J. Herr, O. Wiest, M. Jiang, and N. V. Chawla, “Few-shot graph learning for molecular property prediction,” in Proceedings of the web conference 2021 , 2021, pp. 2559–2567
2021
Cited alongside, same era.
K. Zhang, Q. Liu, Z. Huang, M. Cheng, K. Zhang, M. Zhang, W. Wu, and E. Chen, “Graph adaptive semantic transfer for cross-domain sentiment classification,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2022, pp. 1566–1576
2022
Cited alongside, same era.
A. Madaan, S. Zhou, U. Alon, Y. Yang, and G. Neubig, “Language models of code are few-shot commonsense learners,” arXiv preprint arXiv:2210.07128 , 2022
Original

Then

B. Fatemi, J. Halcrow, and B. Perozzi, “Talk like a graph: Encoding graphs for large language models,” arXiv preprint arXiv:2310.04560 , 2023
Original
2023
Later among the works it cites.
Y. Li, Z. Li, P. Wang, J. Li, X. Sun, H. Cheng, and J. X. Yu, “A survey of graph meets large language model: Progress and future directions,” arXiv preprint arXiv:2311.12399 , 2023
Original
2023
Later among the works it cites.
J. Guo, L. Du, and H. Liu, “Gpt4graph: Can large language models understand graph structured data? an empirical evaluation and benchmarking,” arXiv preprint arXiv:2305.15066 , 2023
Original
2023
Later among the works it cites.
J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat et al. , “Gpt-4 technical report,” arXiv preprint arXiv:2303.08774 , 2023
Original
2023
Later among the works it cites.

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2024

End-to-End Graph Flattening Method for Large Language Models

Hong, Bin, Wu, Jinze, Liu, Jiayu et al.

Understand

In recent years, the breakthrough of Large Language Models (LLMs) offers new ideas for achieving universal methods on graph data.

The common practice of converting graphs into natural language for LLMs, which refers to graph flattening, exhibits good generalizability and interpretability.
However, the poor organization of the textual format results in poor performance in long-distance scenario understanding.
Inspired by human cognitive reasoning habits, we propose a novel method for graph flattening to fit LLMs, termed as End-to-End DAG-Path prompting (EEDP).

Built on

M. Himsolt, “Gml: Graph modelling language,” University of Passau , 1997
1997
Earlier work this paper cites.
G. F. Lawler and V. Limic, Random walk: a modern introduction . Cambridge University Press, 2010, vol. 123
2010
Earlier work this paper cites.
U. Brandes, M. Eiglsperger, J. Lerner, and C. Pich, “Graph markup language (graphml),” 2013
2013
Earlier work this paper cites.
A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining , 2016, pp. 855–864
2016
Earlier work this paper cites.
W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
N. Tandon, B. D. Mishra, K. Sakaguchi, A. Bosselut, and P. Clark, “Wiqa: A dataset for” what if…” reasoning over procedural text,” arXiv preprint arXiv:1909.04739 , 2019
Original
2019
Earlier work this paper cites.
X. Bresson and T. Laurent, “A two-step graph convolutional decoder for molecule generation,” arXiv preprint arXiv:1906.03412 , 2019
Original
2019
Earlier work this paper cites.
L. Yang, Z. Liu, Y. Dou, J. Ma, and P. S. Yu, “Consisrec: Enhancing gnn for social recommendation via consistent neighbor aggregation,” in Proceedings of the 44th international ACM SIGIR conference on Research and development in information retrieval , 2021, pp. 2141–2145
2021
Earlier work this paper cites.

Similar

S. Wang, K. Zhang, L. Wu, H. Ma, R. Hong, and M. Wang, “Privileged graph distillation for cold start recommendation,” in Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2021, pp. 1187–1196
2021
Cited alongside, same era.
Z. Guo, C. Zhang, W. Yu, J. Herr, O. Wiest, M. Jiang, and N. V. Chawla, “Few-shot graph learning for molecular property prediction,” in Proceedings of the web conference 2021 , 2021, pp. 2559–2567
2021
Cited alongside, same era.
K. Zhang, Q. Liu, Z. Huang, M. Cheng, K. Zhang, M. Zhang, W. Wu, and E. Chen, “Graph adaptive semantic transfer for cross-domain sentiment classification,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2022, pp. 1566–1576
2022
Cited alongside, same era.
A. Madaan, S. Zhou, U. Alon, Y. Yang, and G. Neubig, “Language models of code are few-shot commonsense learners,” arXiv preprint arXiv:2210.07128 , 2022
Original

Then

B. Fatemi, J. Halcrow, and B. Perozzi, “Talk like a graph: Encoding graphs for large language models,” arXiv preprint arXiv:2310.04560 , 2023
Original
2023
Later among the works it cites.
Y. Li, Z. Li, P. Wang, J. Li, X. Sun, H. Cheng, and J. X. Yu, “A survey of graph meets large language model: Progress and future directions,” arXiv preprint arXiv:2311.12399 , 2023
Original
2023
Later among the works it cites.
J. Guo, L. Du, and H. Liu, “Gpt4graph: Can large language models understand graph structured data? an empirical evaluation and benchmarking,” arXiv preprint arXiv:2305.15066 , 2023
Original
2023
Later among the works it cites.
J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat et al. , “Gpt-4 technical report,” arXiv preprint arXiv:2303.08774 , 2023
Original
2023
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

2022

Cited alongside, same era.

W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y. Hou, Y. Min, B. Zhang, J. Zhang, Z. Dong et al. , “A survey of large language models,” arXiv preprint arXiv:2303.18223 , 2023

Original

2023

Cited alongside, same era.

Y. Liu, K. Zhang, Z. Huang, K. Wang, Y. Zhang, Q. Liu, and E. Chen, “Enhancing hierarchical text classification through knowledge graph integration,” in Findings of the Association for Computational Linguistics: ACL 2023 , 2023, pp. 5797–5810

2023

Cited alongside, same era.

X. Liu, K. Zhang, Y. Liu, E. Chen, Z. Huang, L. Yue, and J. Yan, “Rhgn: Relation-gated heterogeneous graph network for entity alignment in knowledge graphs,” in Findings of the Association for Computational Linguistics: ACL 2023 , 2023, pp. 8683–8696

2023

Cited alongside, same era.

K. Zhang, D. Zhang, L. Wu, R. Hong, Y. Zhao, and M. Wang, “Label-aware debiased causal reasoning for natural language inference,” AI Open , vol. 5, pp. 70–78, 2024

2024

Closest in time.

Z. Chen, H. Mao, H. Li, W. Jin, H. Wen, X. Wei, S. Wang, D. Yin, W. Fan, H. Liu et al. , “Exploring the potential of large language models (llms) in learning on graphs,” ACM SIGKDD Explorations Newsletter , vol. 25, no. 2, pp. 42–61, 2024

2024

Closest in time.

H. Wang, S. Feng, T. He, Z. Tan, X. Han, and Y. Tsvetkov, “Can language models solve graph problems in natural language?” Advances in Neural Information Processing Systems , vol. 36, 2024

2024

Closest in time.