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Large language models (LLMs) are gaining increasing attention for their capability to process graphs with rich text attributes, especially in a zero-shot fashion.
Political networks: the structural perspective , volume 4
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Automating the construction of internet portals with machine learning
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Birds of a feather: Homophily in social networks
Miller McPherson, Lynn Smith-Lovin, and James M Cook · 2001
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Link-based classification
Qing Lu and Lise Getoor · 2003
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Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
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Distributed representations of words and phrases and their compositionality
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Point biserial correlation
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Homophily and missing links in citation networks
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Semi-supervised classification with graph convolutional networks
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Zhilin Yang, William Cohen, and Ruslan Salakhudinov · 2016
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Review of short-text classification
Issa Alsmadi and Keng Hoon Gan · 2019
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On the use of arxiv as a dataset, 2019
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Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen · 2019
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Language models are few-shot learners, 2020
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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Grale: Designing networks for graph learning
Jonathan Halcrow, Alexandru Mosoi, Sam Ruth, and Bryan Perozzi · 2020
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Graph learning indexer: A contributor-friendly and metadata-rich platform for graph learning benchmarks
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Introducing chatgpt, 2022
OpenAI · 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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Can we trust the evaluation on ChatGPT?
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Exploring the potential of large language models (llms) in learning on graphs
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Open graph benchmark: Datasets for machine learning on graphs
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Microsoft academic graph: When experts are not enough
Kuansan Wang, Zhihong Shen, Chiyuan Huang, Chieh-Han Wu, Yuxiao Dong, and Anshul Kanakia · 2020
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Beyond homophily in graph neural networks: Current limitations and effective designs
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra · 2020
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Extracting training data from large language models
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Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods
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Improving graph neural networks with simple architecture design
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GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
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Beyond text: A deep dive into large language models’ ability on understanding graph data
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On performance discrepancies across local homophily levels in graph neural networks
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Gpt-4 technical report, 2023
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Llama: Open and efficient foundation language models
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Can language models solve graph problems in natural language?
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Natural language is all a graph needs
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Graphtext: Graph reasoning in text space
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