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Large Language Models (LLMs) have demonstrated remarkable performance across various natural language processing tasks.
A cluster separation measure
David L Davies and Donald W Bouldin · 1979
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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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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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Adversarial attacks on neural networks for graph data
Daniel Zügner, Amir Akbarnejad, and Stephan Günnemann · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 2019
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Topology attack and defense for graph neural networks: An optimization perspective
Kaidi Xu, Hongge Chen, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, and Xue Lin · 2019
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Attacking graph convolutional networks via rewiring
Yao Ma, Suhang Wang, Tyler Derr, Lingfei Wu, and Jiliang Tang · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen · 2019
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Adversarial attacks on graph neural networks: Perturbations and their patterns
Daniel Zügner, Oliver Borchert, Amir Akbarnejad, and Stephan Günnemann · 2020
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Towards more practical adversarial attacks on graph neural networks
Jiaqi Ma, Shuangrui Ding, and Qiaozhu Mei · 2020
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Graph structure learning for robust graph neural networks
Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, and Jiliang Tang · 2020
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A restricted black-box adversarial framework towards attacking graph embedding models
Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Wenwu Zhu, and Junzhou Huang · 2020
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All you need is low (rank) defending against adversarial attacks on graphs
Negin Entezari, Saba A Al-Sayouri, Amirali Darvishzadeh, and Evangelos E Papalexakis · 2020
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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
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Wiki-cs: A wikipedia-based benchmark for graph neural networks
Péter Mernyei and Cătălina Cangea · 2020
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Deeprobust: A pytorch library for adversarial attacks and defenses
Yaxin Li, Wei Jin, Han Xu, and Jiliang Tang · 2020
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Graph-based deep learning for medical diagnosis and analysis: past, present and future
David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, and Lars Petersson · 2021
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Adversarial attacks and defenses on graphs
Wei Jin, Yaxing Li, Han Xu, Yiqi Wang, Shuiwang Ji, Charu Aggarwal, and Jiliang Tang · 2021
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Robustness of graph neural networks at scale
Simon Geisler, Tobias Schmidt, Hakan Şirin, Daniel Zügner, Aleksandar Bojchevski, and Stephan Günnemann · 2021
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Jiayan Guo, Lun Du, Hengyu Liu, Mengyu Zhou, Xinyi He, and Shi Han · 2023
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He Cao, Zijing Liu, Xingyu Lu, Yuan Yao, and Yu Li · 2023
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Git-mol: A multi-modal large language model for molecular science with graph
Pengfei Liu, Yiming Ren, and Zhixiang Ren · 2023
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Can large language models empower molecular property prediction?
Chen Qian, Huayi Tang, Zhirui Yang, Hong Liang, and Yong Liu · 2023
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Simteg: A frustratingly simple approach improves textual graph learning
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Time-aware gradient attack on dynamic network link prediction
Jinyin Chen, Jian Zhang, Zhi Chen, Min Du, and Qi Xuan · 2021
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Projective ranking: A transferable evasion attack method on graph neural networks
He Zhang, Bang Wu, Xiangwen Yang, Chuan Zhou, Shuo Wang, Xingliang Yuan, and Shirui Pan · 2021
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Lora: Low-rank adaptation of large language models
Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al · 2021
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Node feature extraction by self-supervised multi-scale neighborhood prediction
Eli Chien, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Jiong Zhang, Olgica Milenkovic, and Inderjit S Dhillon · 2022
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Learning on large-scale text-attributed graphs via variational inference
Jianan Zhao, Meng Qu, Chaozhuo Li, Hao Yan, Qian Liu, Rui Li, Xing Xie, and Jian Tang · 2022
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Semattack: Natural textual attacks via different semantic spaces
Boxin Wang, Chejian Xu, Xiangyu Liu, Yu Cheng, and Bo Li · 2022
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Text embeddings by weakly-supervised contrastive pre-training
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei · 2022
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Keyu Duan, Qian Liu, Tat-Seng Chua, Shuicheng Yan, Wei Tsang Ooi, Qizhe Xie, and Junxian He · 2023
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Review of graph-based hazardous event detection methods for autonomous driving systems
Dannier Xiao, Mehrdad Dianati, William Gonçalves Geiger, and Roger Woodman · 2023
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Natural language is all a graph needs
Ruosong Ye, Caiqi Zhang, Runhui Wang, Shuyuan Xu, and Yongfeng Zhang · 2023
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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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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
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Angle-optimized text embeddings
Xianming Li and Jing Li · 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
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A gpt among annotators: Llm-based entity-level sentiment annotation
Egil Rønningstad, Erik Velldal, and Lilja Øvrelid · 2024
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Improving llm-based machine translation with systematic self-correction
Zhaopeng Feng, Yan Zhang, Hao Li, Wenqiang Liu, Jun Lang, Yang Feng, Jian Wu, and Zuozhu Liu · 2024
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Generation-driven contrastive self-training for zero-shot text classification with instruction-following llm
Ruohong Zhang, Yau-Shian Wang, and Yiming Yang · 2024
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Exploring the potential of large language models (llms) in learning on graphs
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Llmrec: Large language models with graph augmentation for recommendation
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Gimlet: A unified graph-text model for instruction-based molecule zero-shot learning
Haiteng Zhao, Shengchao Liu, Ma Chang, Hannan Xu, Jie Fu, Zhihong Deng, Lingpeng Kong, and Qi Liu · 2024
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Can language models solve graph problems in natural language?
Heng Wang, Shangbin Feng, Tianxing He, Zhaoxuan Tan, Xiaochuang Han, and Yulia Tsvetkov · 2024
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A review of graph neural networks in epidemic modeling
Zewen Liu, Guancheng Wan, B Aditya Prakash, Max SY Lau, and Wei Jin · 2024
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