Network data in real-world tends to be error-prone due to incomplete sampling, imperfect measurements or even malicious attacks. This in turn results in inaccurate results when performing network analysis or modeling on these flawed networks.

Our research aims to reconstruct a reliable network from a flawed one, a process referred to network enhancement. More specifically, network enhancement aims to detect the noisy links that are observed in the network but should not exist in the real world, as well as to complement the missing links that do indeed exist in the real world yet remain unobserved.

From one perspective, we turns the network enhancement problem into edge sequences generation, and employ a deep reinforcement learning framework to solve it, which takes advantage of downstream task to guide the network denoising process (NetRL, Xu et al, TKDE'21). From another perspective, we construct a self-supervised learning framework that identifies missing links and nosiy links simultaneously by leveraging the mutual influence of them (E-Net, Xu et al, TKDE'20).

Furthermore, we study the model robutness against adversarial attacks. Our work shows that even without any information about the target model, one can still perform effective attacks (Xu et al, AAAI'22). To handle the adversarial vulnerability problem, we further propose an unsupervised defense technique to robustify pre-trained deep graph models (Xu et al, AAAI'22).

We propose a general game bot detection framework for massively multiplayer online role playing games termed NGUARD+ (denoting NetEase Games’ Guard), which captures user patterns in order to identify game bots from player behavior sequences. NGUARD+ mainly employs attention-based methods to automatically differentiate game bots from humans. We provide a combination of supervised and unsupervised methods for game bot detection to detect game bots and new type of game bots even when the labels of game bots are limited ([Tao and Xu et al, KDD'18];[Xu et al, TKDD'20]).

Taoran Fang, Zhiqing Xiao, Chunping Wang, Jiarong Xu, Xuan Yang, and Yang Yang. DropMessage: Unifying Random Dropping for Graph Neural Networks. In Proceedings of the 37th Conference on Artificial Intelligence (AAAI'23).

Xuanwen Huang, Yang Yang, Yang Wang, Chunping Wang, Zhisheng Zhang, Jiarong Xu, and Lei Chen. DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection. In Proceedings of the 36th Conference on Neural Information Processing System Track on Datasets and Benchmarks (NeurIPS’22 D&B). [PDF] [DGraph Data]

Yifei Sun, Haoran Deng, Yang Yang, Chunping Wang, Jiarong Xu, Renhong Huang, Linfeng Cao, Yang Wang and Lei Chen. Beyond Homophily: Structure-aware Path Aggregation Graph Neural Network. In Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI'22). [PDF]

Ziwei Chai, Siqi You, Yang Yang, Shiliang Pu, Jiarong Xu, Haoyang Cai and Weihao Jiang . Can Abnormality be Detected by Graph Neural Networks? In Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI'22). [PDF]

Jiarong Xu, Yang Yang, Junru Chen, Xin Jiang, Chunping Wang, Jiangang Lu and Yizhou Sun. Unsupervised Adversarially Robust Representation Learning on Graphs. In Proceedings of the 36th Conference on Artificial Intelligence (AAAI’22). [PDF] [Code]

Jiarong Xu, Yizhou Sun, Xin Jiang, Yanhao Wang, Chunping Wang, Jiangang Lu and Yang Yang. Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs. In Proceedings of the 36th Conference on Artificial Intelligence (AAAI’22). [PDF] [Code]

Qinkai Zheng, Xu Zou, Yuxiao Dong, Yukuo Cen, Da Yin, Jiarong Xu, Yang Yang, and Jie Tang. Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning. In Proceedings of the 35th Conference on Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS’21 D&B). [PDF] [Code]

Jiarong Xu, Yang Yang, Shiliang Pu, Yao Fu, Jun Feng, Weihao Jiang, Jiangang Lu and Cunping Wang. NetRL: Task-aware Network Denoising via Deep Reinforcement Learning. In IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021. [PDF] [Code]

Jiarong Xu, Yang Yang, Chunping Wang, Zongtao Liu, Jing Zhang, Lei Chen and Jiangang Lu. Robust Network Enhancement from Flawed Networks. In IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020. [PDF] [Code]

Jiarong Xu, Yifan Luo, Jianrong Tao, Changjie Fan, Zhou Zhao and Jiangang Lu. NGUARD+: A Attention-based Game Bot Detection Framework via Player Behavior Sequences. In ACM Transactions on Knowledge Discovery from Data (TKDD), 2020. [PDF]

Jianrong Tao*, Jiarong Xu*, Linxia Gong, Yifu Li, Changjie Fan and Zhou Zhao (*: Equal Contribution). NGUARD: A Game Bot Detection Framework for NetEase MMORPGs. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'18). [PDF] [Code]

Yiren Shen, Xiaoxian Ou, Jiarong Xu, Guanglin Zhang, Lin Wang and Dapeng Li. Optimal energy storage management for microgrids with ON/OFF co-generator: A two-time-scale approach. In Proceedings of the 3rd IEEE Conference on Signal and Information Processing (GlobalSIP'15). [PDF]

Chen Chen, Xueyuan Li, Ye Yang, Jiarong Xu, Zuwei Liao, Xinggao Liu and Jinshui Chen and Jiangang Lu. Parameter Self-Tuning of SISO Compact-Form Model-Free Adaptive Controller based on Neural Network with System Error Set as Input. In Proceedings of the 12th Asian Control Conference (ASCC'19). [PDF]

Ye Yang, Jinhou Han, Chen Chen, Jiarong Xu, Zuwei Liao, Xinggao Liu, Jinshui Chen and Jiangang Lu. A PSO-LP Cooperative Algorithm for Mixed Integer Nonlinear Programming. In Proceedings of the 12th Asian Control Conference (ASCC'19). [PDF]

PC Members: KDD, WWW, AAAI, WSDM, ECML-PKDD, SDM

Reviewers: IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Transaction on Big Data, IEEE Transactions on Network and Service Management