Network Embedding with Centrality Information. - Yao Ma 001; Suhang Wang; Jiliang Tang (ICDM Workshops, 2017)

Preserving Local and Global Information for Network Embedding. - Yao Ma 001; Suhang Wang; Zhaochun Ren; Dawei Yin; Jiliang Tang (CoRR, 2017)

Local and Global Information Preserved Network Embedding. - Yao Ma 001; Suhang Wang; Jiliang Tang (ASONAM, 2018)

Signed Graph Convolutional Networks. - Tyler Derr; Yao Ma 001; Jiliang Tang (ICDM, 2018)

Multi-Dimensional Network Embedding with Hierarchical Structure. - Yao Ma 001; Zhaochun Ren; Ziheng Jiang; Jiliang Tang; Dawei Yin (WSDM, 2018)

Linked Recurrent Neural Networks. - Zhiwei Wang 001; Yao Ma 001; Dawei Yin; Jiliang Tang (CoRR, 2018)

Signed Graph Convolutional Network. - Tyler Derr; Yao Ma 001; Jiliang Tang (CoRR, 2018)

Dynamic Graph Neural Networks. - Yao Ma 001; Ziyi Guo; Zhaochun Ren; Yihong Eric Zhao; Jiliang Tang; Dawei Yin (CoRR, 2018)

Deep Adversarial Social Recommendation. - Wenqi Fan; Tyler Derr; Yao Ma 001; Jianping Wang 001; Jiliang Tang; Qing Li 001 (IJCAI, 2019)

Graph Convolutional Networks with EigenPooling. - Yao Ma 001; Suhang Wang; Charu C. Aggarwal; Jiliang Tang (KDD, 2019)

Deep social collaborative filtering. - Wenqi Fan; Yao Ma 001; Dawei Yin; Jianping Wang 001; Jiliang Tang; Qing Li 001 (RecSys, 2019)

Multi-dimensional Graph Convolutional Networks. - Yao Ma 001; Suhang Wang; Charu C. Aggarwal; Dawei Yin; Jiliang Tang (SDM, 2019)

Graph Neural Networks for Social Recommendation. - Wenqi Fan; Yao Ma 001; Qing Li 001; Yuan He; Yihong Eric Zhao; Jiliang Tang; Dawei Yin (WWW, 2019)

Attacking Graph Convolutional Networks via Rewiring. - Yao Ma 001; Suhang Wang; Lingfei Wu 001; Jiliang Tang (CoRR, 2019)

R-Transformer: Recurrent Neural Network Enhanced Transformer. - Zhiwei Wang 001; Yao Ma 001; Zitao Liu 001; Jiliang Tang (CoRR, 2019)

Graph Pooling with Representativeness. - Juan-Hui Li; Yao Ma 001; Yiqi Wang 001; Charu C. Aggarwal; Chang-Dong Wang 001; Jiliang Tang (ICDM, 2020)

Learning from Incomplete Labeled Data via Adversarial Data Generation. - Wentao Wang 006; Tyler Derr; Yao Ma 001; Suhang Wang; Hui Liu 031; Zitao Liu 001; Jiliang Tang (ICDM, 2020)

Graph Structure Learning for Robust Graph Neural Networks. - Wei Jin 009; Yao Ma 001; Xiaorui Liu; Xianfeng Tang; Suhang Wang; Jiliang Tang (KDD, 2020)

Deep Graph Learning: Foundations, Advances and Applications. - Yu Rong 001; Tingyang Xu; Junzhou Huang; Wenbing Huang 001; Hong Cheng 001; Yao Ma 001; Yiqi Wang 001; Tyler Derr; Lingfei Wu 001; Tengfei Ma 001 (KDD, 2020)

Deep Adversarial Canonical Correlation Analysis. - Wenqi Fan; Yao Ma 001; Han Xu 002; Xiaorui Liu; Jianping Wang 001; Qing Li 001; Jiliang Tang (SDM, 2020)

Streaming Graph Neural Networks. - Yao Ma 001; Ziyi Guo; Zhaochun Ren; Jiliang Tang; Dawei Yin (SIGIR, 2020)

Epidemic Graph Convolutional Network. - Tyler Derr; Yao Ma 001; Wenqi Fan; Xiaorui Liu; Charu C. Aggarwal; Jiliang Tang (WSDM, 2020)

Traffic Flow Prediction via Spatial Temporal Graph Neural Network. - Xiaoyang Wang; Yao Ma 001; Yiqi Wang 001; Wei Jin 009; Xin Wang 035; Jiliang Tang; Caiyan Jia; Jian Yu 001 (WWW, 2020)

Non-IID Graph Neural Networks. - Yiqi Wang 001; Yao Ma 001; Charu C. Aggarwal; Jiliang Tang (CoRR, 2020)

Adversarial Attacks and Defenses in Images, Graphs and Text: A Review. - Han Xu 002; Yao Ma 001; Haochen Liu; Debayan Deb; Hui Liu 031; Jiliang Tang; Anil K. Jain 001 (Int. J. Autom. Comput., 2020)

A Unified View on Graph Neural Networks as Graph Signal Denoising. - Yao Ma 001; Xiaorui Liu; Tong Zhao 003; Yozen Liu; Jiliang Tang; Neil Shah (CIKM, 2021)

Graph Feature Gating Networks. - Wei Jin 009; Xiaorui Liu; Yao Ma 001; Tyler Derr; Charu C. Aggarwal; Jiliang Tang (CIKM, 2021)

Attacking Black-box Recommendations via Copying Cross-domain User Profiles. - Wenqi Fan; Tyler Derr; Xiangyu Zhao 001; Yao Ma 001; Hui Liu 031; Jianping Wang 001; Jiliang Tang; Qing Li 001 (ICDE, 2021)

Elastic Graph Neural Networks. - Xiaorui Liu; Wei Jin 009; Yao Ma 001; Yaxin Li 001; Hua Liu; Yiqi Wang 001; Ming Yan 006; Jiliang Tang (ICML, 2021)

Graph Adversarial Attack via Rewiring. - Yao Ma 001; Suhang Wang; Tyler Derr; Lingfei Wu 001; Jiliang Tang (KDD, 2021)

Graph Representation Learning: Foundations, Methods, Applications and Systems. - Wei Jin 009; Yao Ma 001; Yiqi Wang 001; Xiaorui Liu; Jiliang Tang; Yukuo Cen; Jiezhong Qiu; Jie Tang 001; Chuan Shi 001; Yanfang Ye 001; Jiawei Zhang 001; Philip S. Yu (KDD, 2021)

Graph Neural Networks with Adaptive Residual. - Xiaorui Liu; Jiayuan Ding; Wei Jin 009; Han Xu 002; Yao Ma 001; Zitao Liu 001; Jiliang Tang (NeurIPS, 2021)

Node Similarity Preserving Graph Convolutional Networks. - Wei Jin 009; Tyler Derr; Yiqi Wang 001; Yao Ma 001; Zitao Liu 001; Jiliang Tang (WSDM, 2021)

Heterogeneous Randomized Response for Differential Privacy in Graph Neural Networks. - Khang Tran; Phung Lai; NhatHai Phan; Issa Khalil; Yao Ma 001; Abdallah Khreishah; My T. Thai; Xintao Wu (IEEE Big Data, 2022)

SGKD: A Scalable and Effective Knowledge Distillation Framework for Graph Representation Learning. - Yufei He; Yao Ma 001 (ICDM (Workshops), 2022)

Is Homophily a Necessity for Graph Neural Networks? - Yao Ma 001; Xiaorui Liu; Neil Shah; Jiliang Tang (ICLR, 2022)

Automated Self-Supervised Learning for Graphs. - Wei Jin 009; Xiaorui Liu; Xiangyu Zhao 001; Yao Ma 001; Neil Shah; Jiliang Tang (ICLR, 2022)

Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective. - Wei Jin 009; Xiaorui Liu; Yao Ma 001; Charu C. Aggarwal; Jiliang Tang (KDD, 2022)

Are Graph Neural Networks Really Helpful for Knowledge Graph Completion? - Juan-Hui Li; Harry Shomer; Jiayuan Ding; Yiqi Wang 001; Yao Ma 001; Neil Shah; Jiliang Tang; Dawei Yin (CoRR, 2022)

Enhancing Graph Contrastive Learning with Node Similarity. - Hongliang Chi; Yao Ma 001 (CoRR, 2022)

Learning Representations for Hyper-Relational Knowledge Graphs. - Harry Shomer; Wei Jin 009; Juan-Hui Li; Yao Ma 001; Jiliang Tang (CoRR, 2022)

Negative samples selecting strategy for graph contrastive learning. - Rui Miao 003; Yintao Yang; Yao Ma 001; Xin Juan; Haotian Xue 001; Jiliang Tang; Ying Wang 009; Xin Wang 035 (Inf. Sci., 2022)

A Graph Neural Network Framework for Social Recommendations. - Wenqi Fan; Yao Ma 001; Qing Li 001; Jianping Wang 001; Guoyong Cai; Jiliang Tang; Dawei Yin (IEEE Trans. Knowl. Data Eng., 2022)

Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion? - Juanhui Li; Harry Shomer; Jiayuan Ding; Yiqi Wang 001; Yao Ma 001; Neil Shah; Jiliang Tang; Dawei Yin (ACL (1), 2023)

Learning Representations for Hyper-Relational Knowledge Graphs. - Harry Shomer; Wei Jin 009; Juanhui Li; Yao Ma 001; Hui Liu 031 (ASONAM, 2023)

Towards Fair Graph Neural Networks via Graph Counterfactual. - Zhimeng Guo; Jialiang Li 003; Teng Xiao; Yao Ma 001; Suhang Wang (CIKM, 2023)

Distance-Based Propagation for Efficient Knowledge Graph Reasoning. - Harry Shomer; Yao Ma 001; Juanhui Li; Bo Wu 002; Charu C. Aggarwal; Jiliang Tang (EMNLP, 2023)

Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking. - Juanhui Li; Harry Shomer; Haitao Mao; Shenglai Zeng; Yao Ma 001; Neil Shah; Jiliang Tang; Dawei Yin (NeurIPS, 2023)

Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All? - Haitao Mao; Zhikai Chen; Wei Jin 009; Haoyu Han 001; Yao Ma 001; Tong Zhao 003; Neil Shah; Jiliang Tang (NeurIPS, 2023)

Graph Enhanced BERT for Query Understanding. - Juanhui Li; Wei Zeng 008; Suqi Cheng; Yao Ma 001; Jiliang Tang; Shuaiqiang Wang; Dawei Yin (SIGIR, 2023)

The 3rd International Workshop on Machine Learning on Graphs (MLoG). - Tyler Derr; Yao Ma 001; Benedek Rozemberczki; Neil Shah; Shirui Pan (WSDM, 2023)

Improving Fairness of Graph Neural Networks: A Graph Counterfactual Perspective. - Zhimeng Guo; Jialiang Li 003; Teng Xiao; Yao Ma 001; Suhang Wang (CoRR, 2023)

Adaptive Pairwise Encodings for Link Prediction. - Harry Shomer; Yao Ma 001; Haitao Mao; Juanhui Li; Bo Wu 002; Jiliang Tang (CoRR, 2023)

Customized Graph Nerual Networks. - Yiqi Wang 001; Yao Ma 001; Wei Jin 009; Chaozhuo Li; Charu Aggarwal 001; Jiliang Tang (IEEE Data Eng. Bull., 2023)

Adversarial Attacks for Black-Box Recommender Systems via Copying Transferable Cross-Domain User Profiles. - Wenqi Fan; Xiangyu Zhao 001; Qing Li 001; Tyler Derr; Yao Ma 001; Hui Liu 031; Jianping Wang 001; Jiliang Tang (IEEE Trans. Knowl. Data Eng., 2023)

Revisiting Link Prediction: a data perspective. - Haitao Mao; Juanhui Li; Harry Shomer; Bingheng Li; Wenqi Fan; Yao Ma 001; Tong Zhao 003; Neil Shah; Jiliang Tang (ICLR, 2024)

PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Linear Programming. - Bingheng Li; Linxin Yang; Yupeng Chen; Senmiao Wang; Haitao Mao; Qian Chen; Yao Ma 001; Akang Wang; Tian Ding; Jiliang Tang; Ruoyu Sun 001 (ICML, 2024)

Position: Graph Foundation Models Are Already Here. - Haitao Mao; Zhikai Chen; Wenzhuo Tang; Jianan Zhao 002; Yao Ma 001; Tong Zhao 003; Neil Shah; Mikhail Galkin 001; Jiliang Tang (ICML, 2024)

Enhancing Contrastive Learning on Graphs with Node Similarity. - Hongliang Chi; Yao Ma 001 (KDD, 2024)

Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark. - Xiaowei Qian; Zhimeng Guo; Jialiang Li 003; Haitao Mao; Bingheng Li; Suhang Wang; Yao Ma 001 (KDD, 2024)

LPFormer: An Adaptive Graph Transformer for Link Prediction. - Harry Shomer; Yao Ma 001; Haitao Mao; Juanhui Li; Bo Wu 002; Jiliang Tang (KDD, 2024)

A Survey on Safe Multi-Modal Learning Systems. - Tianyi Zhao; Liangliang Zhang; Yao Ma 001; Lu Cheng 001 (KDD, 2024)

Do Neural Scaling Laws Exist on Graph Self-Supervised Learning? - Qian Ma 012; Haitao Mao; Jingzhe Liu; Zhehua Zhang; Chunlin Feng; Yu Song; Yihan Shao; Yao Ma 001 (LoG, 2024)

Active Learning for Graphs with Noisy Structures. - Hongliang Chi; Cong Qi; Suhang Wang; Yao Ma 001 (SDM, 2024)

Multi-Instance Adversarial Attack on GNN-Based Malicious Domain Detection. - Mahmoud Nazzal; Issa Khalil; Abdallah Khreishah; NhatHai Phan; Yao Ma 001 (SP, 2024)

Deciphering Crypto Twitter. - Inwon Kang; Maruf Ahmed Mridul; Abraham Sanders; Yao Ma 001; Thilanka Munasinghe; Aparna Gupta; Oshani Seneviratne (WebSci, 2024)

The 5th International Workshop on Machine Learning on Graphs (MLoG). - Tyler Derr; Yao Ma 001; Kaize Ding; Tong Zhao 003; Nesreen K. Ahmed (WSDM, 2024)

DCAI: Data-centric Artificial Intelligence. - Wei Jin 009; Haohan Wang; Daochen Zha; Qiaoyu Tan; Yao Ma 001; Sharon Li 001; Su-In Lee (WWW (Companion Volume), 2024)

Fast Graph Condensation with Structure-based Neural Tangent Kernel. - Lin Wang 040; Wenqi Fan; Jiatong Li 003; Yao Ma 001; Qing Li 001 (WWW, 2024)

A Survey on Graph Condensation. - Hongjia Xu; Liangliang Zhang; Yao Ma 001; Sheng Zhou 004; Zhuonan Zheng; Jiajun Bu (CoRR, 2024)

A Data Generation Perspective to the Mechanism of In-Context Learning. - Haitao Mao; Guangliang Liu; Yao Ma 001; Rongrong Wang; Jiliang Tang (CoRR, 2024)

Graph Foundation Models. - Haitao Mao; Zhikai Chen; Wenzhuo Tang; Jianan Zhao 002; Yao Ma 001; Tong Zhao 003; Neil Shah; Mikhail Galkin 001; Jiliang Tang (CoRR, 2024)

A Survey on Safe Multi-Modal Learning System. - Tianyi Zhao; Liangliang Zhang; Yao Ma 001; Lu Cheng 001 (CoRR, 2024)

Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks. - Qian Ma 012; Hongliang Chi; Hengrui Zhang; Kay Liu; Zhiwei Zhang; Lu Cheng 001; Suhang Wang; Philip S. Yu; Yao Ma 001 (CoRR, 2024)

Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection. - Yuanchen Bei; Sheng Zhou 004; Jinke Shi; Yao Ma 001; Haishuai Wang; Jiajun Bu (CoRR, 2024)

Revisiting the Message Passing in Heterophilous Graph Neural Networks. - Zhuonan Zheng; Yuanchen Bei; Sheng Zhou 004; Yao Ma 001; Ming Gu 014; Hongjia Xu; Chengyu Lai; Jiawei Chen 007; Jiajun Bu (CoRR, 2024)

A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness. - Fali Wang; Zhiwei Zhang; Xianren Zhang; Zongyu Wu 001; Tzuhao Mo; Qiuhao Lu; Wanjing Wang; Rui Li; Junjie Xu; Xianfeng Tang; Qi He 002; Yao Ma 001; et al. (CoRR, 2024)

Precedence-Constrained Winter Value for Effective Graph Data Valuation. - Hongliang Chi; Wei Jin 009; Charu C. Aggarwal; Yao Ma 001 (ICLR, 2025)

Shapley-Guided Utility Learning for Effective Graph Inference Data Valuation. - Hongliang Chi; Qiong Wu 008; Zhengyi Zhou; Yao Ma 001 (ICLR, 2025)

A Survey to Recent Progress Towards Understanding In-Context Learning. - Haitao Mao; Guangliang Liu; Yao Ma 001; Rongrong Wang; Kristen Marie Johnson; Jiliang Tang (NAACL (Findings), 2025)

Learning to Reduce the Scale of Large Graphs: A Comprehensive Survey. - Hongjia Xu; Liangliang Zhang; Yao Ma 001; Sheng Zhou 004; Zhuonan Zheng; Jiajun Bu (ACM Trans. Knowl. Discov. Data, 2025)

Extending Graph Condensation to Multi-Label Datasets: A Benchmark Study. - Liangliang Zhang; Haoran Bao; Yao Ma 001 (Trans. Mach. Learn. Res., 2025)

Unifying and Optimizing Data Values for Selection via Sequential-Decision-Making. - Hongliang Chi; Qiong Wu 008; Zhengyi Zhou; Jonathan Light; Emily Dodwell; Yao Ma 001 (CoRR, 2025)

Exploring Graph Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation. - Yuxiang Wang; Xinnan Dai; Wenqi Fan; Yao Ma 001 (CoRR, 2025)

Towards Graph Foundation Models: A Transferability Perspective. - Yuxiang Wang; Wenqi Fan; Suhang Wang; Yao Ma 001 (CoRR, 2025)

A Graph Perspective to Probe Structural Patterns of Knowledge in Large Language Models. - Utkarsh Sahu; Zhisheng Qi; Yongjia Lei; Ryan A. Rossi; Franck Dernoncourt; Nesreen K. Ahmed; Mahantesh M. Halappanavar; Yao Ma 001; Yu Wang 160 (CoRR, 2025)

Diagnosing and Addressing Pitfalls in KG-RAG Datasets: Toward More Reliable Benchmarking. - Liangliang Zhang; Zhuorui Jiang; Hongliang Chi; Haoyang Chen; Mohammed Elkoumy; Fali Wang; Qiong Wu 008; Zhengyi Zhou; Shirui Pan; Suhang Wang; Yao Ma 001 (CoRR, 2025)