Active Learning for Wireless IoT Intrusion Detection. - Kai Yang 001; Jie Ren 013; Yanqiao Zhu 001; Weiyi Zhang (IEEE Wirel. Commun., 2018)

Session-Based Recommendation with Graph Neural Networks. - Shu Wu; Yuyuan Tang; Yanqiao Zhu 001; Liang Wang 001; Xing Xie 001; Tieniu Tan (AAAI, 2019)

Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification. - Fenyu Hu; Yanqiao Zhu 001; Shu Wu; Liang Wang 001; Tieniu Tan (IJCAI, 2019)

Tripartite Active Learning for Interactive Anomaly Discovery. - Yanqiao Zhu 001; Kai Yang 001 (IEEE Access, 2019)

Semi-supervised Node Classification via Hierarchical Graph Convolutional Networks. - Fenyu Hu; Yanqiao Zhu 001; Shu Wu; Liang Wang 001; Tieniu Tan (CoRR, 2019)

TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation. - Feng Yu 001; Yanqiao Zhu 001; Qiang Liu 006; Shu Wu; Liang Wang 001; Tieniu Tan (SIGIR, 2020)

Deep Graph Contrastive Representation Learning. - Yanqiao Zhu 001; Yichen Xu; Feng Yu 001; Qiang Liu 006; Shu Wu; Liang Wang 001 (CoRR, 2020)

CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning. - Yanqiao Zhu 001; Yichen Xu; Feng Yu 001; Shu Wu; Liang Wang 001 (CoRR, 2020)

Deep Active Graph Representation Learning. - Yanqiao Zhu 001; Weizhi Xu 002; Feng Yu 001; Qiang Liu 006; Shu Wu; Liang Wang 001 (CoRR, 2020)

Deep Active Learning for Text Classification with Diverse Interpretations. - Qiang Liu 006; Yanqiao Zhu 001; Zhaocheng Liu; Yufeng Zhang; Shu Wu (CIKM, 2021)

Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction. - Yichen Xu; Yanqiao Zhu 001; Feng Yu 001; Qiang Liu 006; Shu Wu (CIKM, 2021)

Mining Latent Structures for Multimedia Recommendation. - Jinghao Zhang; Yanqiao Zhu 001; Qiang Liu 006; Shu Wu; Shuhui Wang; Liang Wang 001 (ACM Multimedia, 2021)

An Empirical Study of Graph Contrastive Learning. - Yanqiao Zhu 001; Yichen Xu; Qiang Liu 006; Shu Wu (NeurIPS Datasets and Benchmarks, 2021)

Graph Contrastive Learning with Adaptive Augmentation. - Yanqiao Zhu 001; Yichen Xu; Feng Yu 001; Qiang Liu 006; Shu Wu; Liang Wang 001 (WWW, 2021)

Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction. - Yanqiao Zhu 001; Yichen Xu; Feng Yu 001; Qiang Liu 006; Shu Wu; Liang Wang 001 (CoRR, 2021)

Deep Graph Structure Learning for Robust Representations: A Survey. - Yanqiao Zhu 001; Weizhi Xu 002; Jinghao Zhang; Qiang Liu 006; Shu Wu; Liang Wang 001 (CoRR, 2021)

Graph Symbiosis Learning. - Liang Zeng 002; Jin Xu 010; Zijun Yao 002; Yanqiao Zhu 001; Jian Li 015 (CoRR, 2021)

BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis. - Hejie Cui; Wei Dai 013; Yanqiao Zhu 001; Xiaoxiao Li; Lifang He 001; Carl Yang 001 (CoRR, 2021)

Deep Contrastive Learning for Multi-View Network Embedding. - Mengqi Zhang; Yanqiao Zhu 001; Shu Wu; Liang Wang 001 (CoRR, 2021)

Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning. - Yanqiao Zhu 001; Yichen Xu; Hejie Cui; Carl Yang 001; Qiang Liu 006; Shu Wu (CoRR, 2021)

Latent Structures Mining with Contrastive Modality Fusion for Multimedia Recommendation. - Jinghao Zhang; Yanqiao Zhu 001; Qiang Liu 006; Mengqi Zhang; Shu Wu; Liang Wang 001 (CoRR, 2021)

GraphAIR: Graph representation learning with neighborhood aggregation and interaction. - Fenyu Hu; Yanqiao Zhu 001; Shu Wu; Weiran Huang 002; Liang Wang 001; Tieniu Tan (Pattern Recognit., 2021)

BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks (Extended Abstract). - Hejie Cui; Wei Dai 013; Yanqiao Zhu 001; Xuan Kan; Antonio Aodong Chen Gu; Joshua Lukemire; Liang Zhan; Lifang He 001; Ying Guo 003; Carl Yang 001 (IEEE Big Data, 2022)

Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks (Extended Abstract). - Yue Yu 001; Xuan Kan; Hejie Cui; Ran Xu 002; Yujia Zheng 001; Xiangchen Song; Yanqiao Zhu 001; Kun Zhang 001; Razieh Nabi; Ying Guo 003; Chao Zhang 014; Carl Yang 001 (IEEE Big Data, 2022)

Deep Contrastive Multiview Network Embedding. - Mengqi Zhang; Yanqiao Zhu 001; Qiang Liu 006; Shu Wu; Liang Wang 001 (CIKM, 2022)

Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis. - Yanqiao Zhu 001; Hejie Cui; Lifang He 001; Lichao Sun 001; Carl Yang 001 (EMBC, 2022)

Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning. - Yi Yang 094; Yanqiao Zhu 001; Hejie Cui; Xuan Kan; Lifang He 001; Ying Guo 003; Carl Yang 001 (KDD, 2022)

A Survey on Deep Graph Generation: Methods and Applications. - Yanqiao Zhu 001; Yuanqi Du; Yinkai Wang; Yichen Xu; Jieyu Zhang 001; Qiang Liu 006; Shu Wu (LoG, 2022)

The First Learning on Graphs Conference: Preface. - Bastian Rieck; Razvan Pascanu; Yuanqi Du; Hannes Stärk; Derek Lim; Chaitanya K. Joshi; Andreea Deac; Iulia Duta; Joshua Robinson 001; Gabriele Corso; Leonardo Cotta; Yanqiao Zhu 001; et al. (LoG, 2022)

Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis. - Hejie Cui; Wei Dai 013; Yanqiao Zhu 001; Xiaoxiao Li; Lifang He 001; Carl Yang 001 (MICCAI (8), 2022)

Structure-Enhanced Heterogeneous Graph Contrastive Learning. - Yanqiao Zhu 001; Yichen Xu; Hejie Cui; Carl Yang 001; Qiang Liu 006; Shu Wu (SDM, 2022)

A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications. - Jun Xia 001; Yanqiao Zhu 001; Yuanqi Du; Stan Z. Li (CoRR, 2022)

Improving Molecular Pretraining with Complementary Featurizations. - Yanqiao Zhu 001; Dingshuo Chen; Yuanqi Du; Yingze Wang; Qiang Liu 006; Shu Wu (CoRR, 2022)

A Systematic Survey of Molecular Pre-trained Models. - Jun Xia 001; Yanqiao Zhu 001; Yuanqi Du; Yue Liu 008; Stan Z. Li (CoRR, 2022)

Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks. - Yue Yu 001; Xuan Kan; Hejie Cui; Ran Xu 002; Yujia Zheng 001; Xiangchen Song; Yanqiao Zhu 001; Kun Zhang 001; Razieh Nabi; Ying Guo 003; Chao Zhang 014; Carl Yang 001 (CoRR, 2022)

A Survey on Pretrained Language Models for Neural Code Intelligence. - Yichen Xu; Yanqiao Zhu 001 (CoRR, 2022)

Neighborhood-Regularized Self-Training for Learning with Few Labels. - Ran Xu 002; Yue Yu 001; Hejie Cui; Xuan Kan; Yanqiao Zhu 001; Joyce C. Ho; Chao Zhang 014; Carl Yang 001 (AAAI, 2023)

AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange. - Liang Zeng 002; Jin Xu 010; Zijun Yao 002; Yanqiao Zhu 001; Jian Li 015 (CIKM, 2023)

A Systematic Survey of Chemical Pre-trained Models. - Jun Xia 001; Yanqiao Zhu 001; Yuanqi Du; Stan Z. Li (IJCAI, 2023)

Deep Dag Learning of Effective Brain Connectivity for FMRI Analysis. - Yue Yu 001; Xuan Kan; Hejie Cui; Ran Xu 002; Yujia Zheng 001; Xiangchen Song; Yanqiao Zhu 001; Kun Zhang 001; Razieh Nabi; Ying Guo 003; Chao Zhang 014; Carl Yang 001 (ISBI, 2023)

The Second Learning on Graphs Conference: Preface. - Soledad Villar; Benjamin Paul Chamberlain; Yuanqi Du; Hannes Stärk; Chaitanya K. Joshi; Andreea Deac; Iulia Duta; Joshua Robinson 001; Yanqiao Zhu 001; Kexin Huang; Michelle M. Li; Sofia Bourhim; et al. (LoG, 2023)

Uncovering Neural Scaling Laws in Molecular Representation Learning. - Dingshuo Chen; Yanqiao Zhu 001; Jieyu Zhang 001; Yuanqi Du; Zhixun Li; Qiang Liu 006; Shu Wu; Liang Wang 056 (NeurIPS, 2023)

M2Hub: Unlocking the Potential of Machine Learning for Materials Discovery. - Yuanqi Du; Yingheng Wang; Yining Huang; Jianan Canal Li; Yanqiao Zhu 001; Tian Xie; Chenru Duan; John M. Gregoire; Carla Pedro Gomes (NeurIPS, 2023)

GSLB: The Graph Structure Learning Benchmark. - Zhixun Li; Xin Sun; Yifan Luo; Yanqiao Zhu 001; Dingshuo Chen; Yingtao Luo; Xiangxin Zhou; Qiang Liu 006; Shu Wu; Liang Wang 001; Jeffrey Xu Yu (NeurIPS, 2023)

Code Recommendation for Open Source Software Developers. - Yiqiao Jin; Yunsheng Bai; Yanqiao Zhu 001; Yizhou Sun; Wei Wang 010 (WWW, 2023)

Uncovering Neural Scaling Laws in Molecular Representation Learning. - Dingshuo Chen; Yanqiao Zhu 001; Jieyu Zhang 001; Yuanqi Du; Zhixun Li; Qiang Liu 006; Shu Wu; Liang Wang 001 (CoRR, 2023)

GSLB: The Graph Structure Learning Benchmark. - Zhixun Li; Liang Wang 056; Xin Sun; Yifan Luo; Yanqiao Zhu 001; Dingshuo Chen; Yingtao Luo; Xiangxin Zhou; Qiang Liu 006; Shu Wu; Liang Wang 001; Jeffrey Xu Yu (CoRR, 2023)

BrainGB: A Benchmark for Brain Network Analysis With Graph Neural Networks. - Hejie Cui; Wei Dai 013; Yanqiao Zhu 001; Xuan Kan; Antonio Aodong Chen Gu; Joshua Lukemire; Liang Zhan; Lifang He 001; Ying Guo 003; Carl Yang 001 (IEEE Trans. Medical Imaging, 2023)

Latent Structure Mining With Contrastive Modality Fusion for Multimedia Recommendation. - Jinghao Zhang; Yanqiao Zhu 001; Qiang Liu 006; Mengqi Zhang; Shu Wu; Liang Wang 001 (IEEE Trans. Knowl. Data Eng., 2023)

Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining. - Yanqiao Zhu 001; Yichen Xu; Feng Yu 001; Qiang Liu 006; Shu Wu (ACM Trans. Intell. Syst. Technol., 2023)

BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers. - Ran Xu 002; Wenqi Shi; Yue Yu 001; Yuchen Zhuang; Yanqiao Zhu 001; May Dongmei Wang; Joyce C. Ho; Chao Zhang 014; Carl Yang 001 (EMNLP, 2024)

Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks. - Yanqiao Zhu 001; Jeehyun Hwang; Keir Adams; Zhen Liu; Bozhao Nan; Brock Stenfors; Yuanqi Du; Jatin Chauhan; Olaf Wiest; Olexandr Isayev; Connor W. Coley; Yizhou Sun; et al. (ICLR, 2024)

SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models. - Xiaoxuan Wang; Ziniu Hu; Pan Lu; Yanqiao Zhu 001; Jieyu Zhang 001; Satyen Subramaniam; Arjun R. Loomba; Shichang Zhang; Yizhou Sun; Wei Wang 010 (ICML, 2024)

An Evaluation of Large Language Models in Bioinformatics Research. - Hengchuang Yin; Zhonghui Gu; Fanhao Wang; Yiparemu Abuduhaibaier; Yanqiao Zhu 001; Xinming Tu; Xian-Sheng Hua 001; Xiao Luo 001; Yizhou Sun (CoRR, 2024)

Efficient Evolutionary Search Over Chemical Space with Large Language Models. - Haorui Wang; Marta Skreta; Cher Tian Ser; Wenhao Gao 001; Lingkai Kong; Felix Streith-Kalthoff; Chenru Duan; Yuchen Zhuang; Yue Yu 001; Yanqiao Zhu 001; Yuanqi Du; Alán Aspuru-Guzik; et al. (CoRR, 2024)

MIRAI: Evaluating LLM Agents for Event Forecasting. - Chenchen Ye 001; Ziniu Hu; Yihe Deng; Zijie Huang 002; Mingyu Derek Ma; Yanqiao Zhu 001; Wei Wang 010 (CoRR, 2024)

Molecular Contrastive Pretraining with Collaborative Featurizations. - Yanqiao Zhu 001; Dingshuo Chen; Yuanqi Du; Yingze Wang; Qiang Liu 006; Shu Wu (J. Chem. Inf. Model., 2024)

Bi-Level Graph Structure Learning for Next POI Recommendation. - Liang Wang 056; Shu Wu; Qiang Liu 006; Yanqiao Zhu 001; Xiang Tao; Mengdi Zhang 002; Liang Wang 001 (IEEE Trans. Knowl. Data Eng., 2024)

Efficient Evolutionary Search Over Chemical Space with Large Language Models. - Haorui Wang; Marta Skreta; Cher Tian Ser; Wenhao Gao 001; Lingkai Kong; Felix Strieth-Kalthoff; Chenru Duan; Yuchen Zhuang; Yue Yu 001; Yanqiao Zhu 001; Yuanqi Du; Alán Aspuru-Guzik; et al. (ICLR, 2025)

Large Language Models Are Innate Crystal Structure Generators. - Jingru Gan; Peichen Zhong; Yuanqi Du; Yanqiao Zhu 001; Chenru Duan; Haorui Wang; Carla P. Gomes; Kristin A. Persson; Daniel Schwalbe-Koda; Wei Wang 010 (CoRR, 2025)