Fetching the paper…
Reading the bibliography…
Recently, research on Text-Attributed Graphs (TAGs) has gained significant attention due to the prevalence of free-text node features in real-world applications and the advancements in Large Language Models (LLMs) that bolster TAG methodologies.
CiteSeer: An automatic citation indexing system. In Proceedings of the third ACM conference on Digital libraries
C Lee Giles, Kurt D Bollacker, and Steve Lawrence. 1998 · 1998
Earlier work this paper cites.
GraphML progress report structural layer proposal: Structural layer proposal. In Graph Drawing: 9th International Symposium, GD 2001 Vienna, Austria, September 23–26, 2001 Revised Papers 9 . Springer, 501–512
Ulrik Brandes, Markus Eiglsperger, Ivan Herman, Michael Himsolt, and M Scott Marshall. 2002 · 2001
Earlier work this paper cites.
Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. 2008 · 2008
Earlier work this paper cites.
Revisiting semi-supervised learning with graph embeddings. In Proc. of ICML
Zhilin Yang, William Cohen, and Ruslan Salakhudinov. 2016 · 2016
Earlier work this paper cites.
Semi-Supervised Classification with Graph Convolutional Networks. In Proc. of ICLR
Thomas N. Kipf and Max Welling. 2017 · 2017
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
Earlier work this paper cites.
Graph Attention Networks. In Proc. of ICLR
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
Earlier work this paper cites.
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. 2019 · 2019
Earlier work this paper cites.
Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks. In Proc. of KDD
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. 2019 · 2019
Earlier work this paper cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proc. of AACL
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In Proc. of EMNLP
Jianmo Ni, Jiacheng Li, and Julian McAuley. 2019 · 2019
Earlier work this paper cites.
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proc. of EMNLP
Nils Reimers and Iryna Gurevych. 2019 · 2019
Earlier work this paper cites.
Adversarial training for free!
Ali Shafahi, Mahyar Najibi, Mohammad Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein. 2019 · 2019
Earlier work this paper cites.
Deep Graph Infomax. In Proc. of ICLR
Petar Velickovic, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, and R. Devon Hjelm. 2019 · 2019
Earlier work this paper cites.
Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020 · 2020
Earlier work this paper cites.
Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. 2020 · 2020
Earlier work this paper cites.
Adversarial self-supervised contrastive learning
Minseon Kim, Jihoon Tack, and Sung Ju Hwang. 2020b · 2020
Earlier work this paper cites.
Multimodal Post Attentive Profiling for Influencer Marketing. In Proceedings of The Web Conference 2020 (Taipei, Taiwan) (WWW ’20) . Association for Computing Machinery, New York, NY, USA, 2878–2884
Seungbae Kim, Jyun-Yu Jiang, Masaki Nakada, Jinyoung Han, and Wei Wang. 2020a · 2020
Earlier work this paper cites.
Wiki-cs: A wikipedia-based benchmark for graph neural networks
Péter Mernyei and Cătălina Cangea. 2020 · 2020
Earlier work this paper cites.
Microsoft academic graph: When experts are not enough
Kuansan Wang, Zhihong Shen, Chiyuan Huang, Chieh-Han Wu, Yuxiao Dong, and Anshul Kanakia. 2020a · 2020
Earlier work this paper cites.
Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In Proc. of ICML
Tongzhou Wang and Phillip Isola. 2020 · 2020
Earlier work this paper cites.
Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers
Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou. 2020b · 2020
Earlier work this paper cites.
Unsupervised domain adaptive graph convolutional networks. In Proc. of WWW
Man Wu, Shirui Pan, Chuan Zhou, Xiaojun Chang, and Xingquan Zhu. 2020 · 2020
Earlier work this paper cites.
Deep Graph Contrastive Representation Learning. In ICML Workshop on Graph Representation Learning and Beyond
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. 2020 · 2020
Earlier work this paper cites.
Danny Hernandez, Jared Kaplan, Tom Henighan, and Sam McCandlish. 2021 · 2021
Earlier work this paper cites.
LoRA: Low-Rank Adaptation of Large Language Models. In Proc. of ICLR
Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al · 2021
Cited alongside, same era.
Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models
Dejun Jiang, Zhenxing Wu, Chang-Yu Hsieh, Guangyong Chen, Ben Liao, Zhe Wang, Chao Shen, Dongsheng Cao, Jian Wu, and Tingjun Hou. 2021 · 2021
Cited alongside, same era.
Revisiting catastrophic forgetting in class incremental learning
Zixuan Ni, Haizhou Shi, Siliang Tang, Longhui Wei, Qi Tian, and Yueting Zhuang. 2021 · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision. In International conference on machine learning . PMLR, 8748–8763
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
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.
Augmenting low-resource text classification with graph-grounded pre-training and prompting. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval . 506–516
Zhihao Wen and Yuan Fang. 2023 · 2023
Later among the works it cites.
Lpml: Llm-prompting markup language for mathematical reasoning
Ryutaro Yamauchi, Sho Sonoda, Akiyoshi Sannai, and Wataru Kumagai. 2023 · 2023
Later among the works it cites.
A Comprehensive Study on Text-attributed Graphs: Benchmarking and Rethinking. In Proc. of NeurIPS
Hao Yan, Chaozhuo Li, Ruosong Long, Chao Yan, Jianan Zhao, Wenwen Zhuang, Jun Yin, Peiyan Zhang, Weihao Han, Hao Sun, et al · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Susheel Suresh, Pan Li, Cong Hao, and Jennifer Neville. 2021 · 2021
Cited alongside, same era.
Bootstrapped representation learning on graphs. In ICLR 2021 Workshop on Geometrical and Topological Representation Learning
Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Rémi Munos, Petar Veličković, and Michal Valko. 2021 · 2021
Cited alongside, same era.
GraphFormers: GNN-nested transformers for representation learning on textual graph
Junhan Yang, Zheng Liu, Shitao Xiao, Chaozhuo Li, Defu Lian, Sanjay Agrawal, Amit Singh, Guangzhong Sun, and Xing Xie. 2021 · 2021
Cited alongside, same era.
Prompt tuning for graph neural networks
Taoran Fang, Yunchao Zhang, Yang Yang, and Chunping Wang. 2022 · 2022
Cited alongside, same era.
Graphmae: Self-supervised masked graph autoencoders. In Proc. of KDD
Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, and Jie Tang. 2022 · 2022
Cited alongside, same era.
Robust optimization as data augmentation for large-scale graphs. In Proc. of CVPR
Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, and Tom Goldstein. 2022 · 2022
Cited alongside, same era.
Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International conference on machine learning . PMLR, 12888–12900
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. 2022 · 2022
Cited alongside, same era.
Pre-Train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2022 · 2022
Cited alongside, same era.
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al · 2023
Later among the works it cites.
SGL-PT: A Strong Graph Learner with Graph Prompt Tuning
Yun Zhu, Jianhao Guo, and Siliang Tang. 2023 · 2023
Later among the works it cites.
Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights
Zhikai Chen, Haitao Mao, Jingzhe Liu, Yu Song, Bingheng Li, Wei Jin, Bahare Fatemi, Anton Tsitsulin, Bryan Perozzi, Hui Liu, et al · 2024
Closest in time.
TAGLAS: An atlas of text-attributed graph datasets in the era of large graph and language models
Jiarui Feng, Hao Liu, Lecheng Kong, Yixin Chen, and Muhan Zhang. 2024 · 2024
Closest in time.
Can GNN be Good Adapter for LLMs? (WWW ’24) . Association for Computing Machinery, New York, NY, USA, 893–904
Xuanwen Huang, Kaiqiao Han, Yang Yang, Dezheng Bao, Quanjin Tao, Ziwei Chai, and Qi Zhu. 2024 · 2024
Closest in time.
A Survey of Graph Meets Large Language Model: Progress and Future Directions. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24 , Kate Larson (Ed.). International Joint Conferences on Artificial Intelligence Organization, 8123–8131
Yuhan Li, Zhixun Li, Peisong Wang, Jia Li, Xiangguo Sun, Hong Cheng, and Jeffrey Xu Yu. 2024a · 2024
Closest in time.
GLBench: A Comprehensive Benchmark for Graph with Large Language Models
Yuhan Li, Peisong Wang, Xiao Zhu, Aochuan Chen, Haiyun Jiang, Deng Cai, Victor Wai Kin Chan, and Jia Li. 2024c · 2024
Closest in time.
Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2024b · 2024
Closest in time.
Towards Unified Multimodal Editing with Enhanced Knowledge Collaboration
Kaihang Pan, Zhaoyu Fan, Juncheng Li, Qifan Yu, Hao Fei, Siliang Tang, Richang Hong, Hanwang Zhang, and Qianru Sun. 2024a · 2024
Closest in time.
Auto-Encoding Morph-Tokens for Multimodal LLM
Kaihang Pan, Siliang Tang, Juncheng Li, Zhaoyu Fan, Wei Chow, Shuicheng Yan, Tat-Seng Chua, Yueting Zhuang, and Hanwang Zhang. 2024b · 2024
Closest in time.
Subgraph Retrieval Enhanced by Graph-Text Alignment for Commonsense Question Answering. In Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part VI (Lecture Notes in Computer Science, Vol. 14946) . Springer, 39–56
Boci Peng, Yongchao Liu, Xiaohe Bo, Sheng Tian, Baokun Wang, Chuntao Hong, and Yan Zhang. 2024a · 2024
Closest in time.
Continual Learning of Large Language Models: A Comprehensive Survey
Haizhou Shi, Zihao Xu, Hengyi Wang, Weiyi Qin, Wenyuan Wang, Yibin Wang, Zifeng Wang, Sayna Ebrahimi, and Hao Wang. 2024 · 2024
Closest in time.
Graphgpt: Graph instruction tuning for large language models. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval . 491–500
Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Lixin Su, Suqi Cheng, Dawei Yin, and Chao Huang. 2024 · 2024
Closest in time.
Llama 3.1: An In-Depth Analysis of the Next-Generation Large Language Model
Raja Vavekanand and Kira Sam. 2024 · 2024
Closest in time.
An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, Chengpeng Li, Chengyuan Li, Dayiheng Liu, Fei Huang, et al · 2024
Closest in time.
Enhancing text-based knowledge graph completion with zero-shot large language models: A focus on semantic enhancement
Rui Yang, Jiahao Zhu, Jianping Man, Li Fang, and Yi Zhou. 2024b · 2024
Closest in time.
Rui Yang, Jiahao Zhu, Jianping Man, Li Fang, and Yi Zhou. 2024c · 2024
Closest in time.
Vision-language models for vision tasks: A survey
Jingyi Zhang, Jiaxing Huang, Sheng Jin, and Shijian Lu. 2024 · 2024
Closest in time.
Revisiting the domain shift and sample uncertainty in multi-source active domain transfer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 16751–16761
Wenqiao Zhang and Zheqi Lv. 2024 · 2024
Closest in time.
Efficient Tuning and Inference for Large Language Models on Textual Graphs. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24 , Kate Larson (Ed.). International Joint Conferences on Artificial Intelligence Organization, 5734–5742
Yun Zhu, Yaoke Wang, Haizhou Shi, and Siliang Tang. 2024b · 2024
Closest in time.
Optimize Incompatible Parameters through Compatibility-aware Knowledge Integration
Zheqi Lv, Keming Ye, Zishu Wei, Qi Tian, Shengyu Zhang, Wenqiao Zhang, Wenjie Wang, Kun Kuang, Tat-Seng Chua, and Fei Wu. 2025a · 2025
Closest in time.
Zheqi Lv, Tianyu Zhan, Wenjie Wang, Xinyu Lin, Shengyu Zhang, Wenqiao Zhang, Jiwei Li, Kun Kuang, and Fei Wu. 2025b · 2025
Closest in time.