Fetching the paper…
Reading the bibliography…
Real-world data is represented in both structured (e.g., graph connections) and unstructured (e.g., textual, visual information) formats, encompassing complex relationships that include explicit links (such as social connections and user behaviors) and implicit interdependencies among semantic entities, often illustrated through knowledge graphs.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
Earlier work this paper cites.
Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
Earlier work this paper cites.
Inductive representation learning on large graphs
William L. Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
Earlier work this paper cites.
Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
Earlier work this paper cites.
Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, et al · 2018
Earlier work this paper cites.
Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen · 2019
Earlier work this paper cites.
Heterogeneous graph neural network
Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, and Nitesh V. Chawla · 2019
Earlier work this paper cites.
Simple and deep graph convolutional networks
Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li · 2020
Earlier work this paper cites.
Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding
Xinyu Fu, Jiani Zhang, Ziqiao Meng, and Irwin King · 2020
Earlier work this paper cites.
Graph representation learning
William L Hamilton · 2020
Earlier work this paper cites.
Heterogeneous graph transformer
Ziniu Hu, Yuxiao Dong, Kuansan Wang, and Yizhou Sun · 2020
Earlier work this paper cites.
Graph structure learning for robust graph neural networks
Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, and Jiliang Tang · 2020
Earlier work this paper cites.
A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
Earlier work this paper cites.
Curriculum learning for natural language understanding
Benfeng Xu, Licheng Zhang, Zhendong Mao, Quan Wang, Hongtao Xie, and Yongdong Zhang · 2020
Earlier work this paper cites.
Understanding negative sampling in graph representation learning
Zhen Yang, Ming Ding, Chang Zhou, Hongxia Yang, Jingren Zhou, and Jie Tang · 2020
Cited alongside, same era.
Towards robust graph neural networks for noisy graphs with sparse labels
Enyan Dai, Wei Jin, Hui Liu, and Suhang Wang · 2022
Cited alongside, same era.
Revisiting graph contrastive learning from the perspective of graph spectrum
Nian Liu, Xiao Wang, Deyu Bo, Chuan Shi, and Jian Pei · 2022
Cited alongside, same era.
A review-aware graph contrastive learning framework for recommendation
Jie Shuai, Kun Zhang, Le Wu, Peijie Sun, Richang Hong, Meng Wang, and Yong Li · 2022
Cited alongside, same era.
A survey on heterogeneous graph embedding: methods, techniques, applications and sources
Xiao Wang, Deyu Bo, Chuan Shi, Shaohua Fan, Yanfang Ye, and S Yu Philip · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Knowledge-augmented graph machine learning for drug discovery: From precision to interpretability
Zhiqiang Zhong and Davide Mottin · 2023
Later among the works it cites.
Edgi: Equivariant diffusion for planning with embodied agents
Johann Brehmer, Joey Bose, Pim De Haan, and Taco S Cohen · 2024
Closest in time.
Cogagent: A visual language model for gui agents
Wenyi Hong, Weihan Wang, Qingsong Lv, Jiazheng Xu, Wenmeng Yu, Junhui Ji, Yan Wang, Zihan Wang, Yuxiao Dong, Ming Ding, et al · 2024
Closest in time.
Swe-bench: Can language models resolve real-world github issues?
Carlos E Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik R Narasimhan · 2024
Closest in time.
Visualwebarena: Evaluating multimodal agents on realistic visual web tasks
Jing Yu Koh, Robert Lo, Lawrence Jang, Vikram Duvvur, Ming Chong Lim, Po-Yu Huang, Graham Neubig, Shuyan Zhou, Ruslan Salakhutdinov, and Daniel Fried · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Cited alongside, same era.
Graph attention multi-layer perceptron
Wentao Zhang, Ziqi Yin, Zeang Sheng, Yang Li, Wen Ouyang, Xiaosen Li, Yangyu Tao, Zhi Yang, and Bin Cui · 2022
Cited alongside, same era.
Web-scale academic name disambiguation: the whoiswho benchmark, leaderboard, and toolkit
Bo Chen, Jing Zhang, Fanjin Zhang, Tianyi Han, Yuqing Cheng, Xiaoyan Li, Yuxiao Dong, and Jie Tang · 2023
Cited alongside, same era.
Benchmarking graph neural networks
Vijay Prakash Dwivedi, Chaitanya K Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2023
Cited alongside, same era.
Relational deep learning: Graph representation learning on relational databases
Matthias Fey, Weihua Hu, Kexin Huang, Jan Eric Lenssen, Rishabh Ranjan, Joshua Robinson, Rex Ying, Jiaxuan You, and Jure Leskovec · 2023
Cited alongside, same era.
Gat-mf: Graph attention mean field for very large scale multi-agent reinforcement learning
Qianyue Hao, Wenzhen Huang, Tao Feng, Jian Yuan, and Yong Li · 2023
Cited alongside, same era.
Xiaoxin He, Xavier Bresson, Thomas Laurent, Adam Perold, Yann LeCun, and Bryan Hooi · 2023
Cited alongside, same era.
Yuhan Li, Peisong Wang, Zhixun Li, Jeffrey Xu Yu, and Jia Li · 2024
Closest in time.
Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 2024
Closest in time.
The llama 3 herd of models, 2024
AI @ Meta Llama Team · 2024
Closest in time.
Llmscore: Unveiling the power of large language models in text-to-image synthesis evaluation
Yujie Lu, Xianjun Yang, Xiujun Li, Xin Eric Wang, and William Yang Wang · 2024
Closest in time.
Graph foundation models
Haitao Mao, Zhikai Chen, Wenzhuo Tang, Jianan Zhao, Yao Ma, Tong Zhao, Neil Shah, Michael Galkin, and Jiliang Tang · 2024
Closest in time.
Graph convolutional kernel machine versus graph convolutional networks
Zhihao Wu, Zhao Zhang, and Jicong Fan · 2024
Closest in time.
Anygraph: Graph foundation model in the wild
Lianghao Xia and Chao Huang · 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.
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, et al · 2024
Closest in time.
Heterogeneous graph attention network
Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S Yu · 2032
Closest in time.
Heterogeneous graph attention network
Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, et al · 2032
Closest in time.