1 code implementation • 18 May 2024 • Yingguang Yang, Qi Wu, Buyun He, Hao Peng, Renyu Yang, Zhifeng Hao, Yong Liao
Recent advancements in social bot detection have been driven by the adoption of Graph Neural Networks.
1 code implementation • 23 Apr 2024 • Buyun He, Yingguang Yang, Qi Wu, Hao liu, Renyu Yang, Hao Peng, Xiang Wang, Yong Liao, Pengyuan Zhou
To tackle these challenges, we propose BotDGT, a novel framework that not only considers the topological structure, but also effectively incorporates dynamic nature of social network.
2 code implementations • 17 Mar 2023 • Dongcheng Zou, Hao Peng, Xiang Huang, Renyu Yang, JianXin Li, Jia Wu, Chunyang Liu, Philip S. Yu
Graph Neural Networks (GNNs) are de facto solutions to structural data learning.
1 code implementation • 10 Mar 2023 • Yingguang Yang, Renyu Yang, Hao Peng, Yangyang Li, Tong Li, Yong Liao, Pengyuan Zhou
In particular, a global generator is used to extract the knowledge of global data distribution and distill it into each client's local model.
1 code implementation • 16 Feb 2023 • Jiayu Zhao, Renyu Yang, Shenghao Qiu, Zheng Wang
Bayesian optimization (BO) is widely used to optimize expensive-to-evaluate black-box functions. BO first builds a surrogate model to represent the objective function and assesses its uncertainty.
1 code implementation • 14 Jun 2022 • Yingguang Yang, Renyu Yang, Yangyang Li, Kai Cui, Zhiqin Yang, Yue Wang, Jie Xu, Haiyong Xie
More specifically, we consider the social bot detection problem as a user-centric subgraph embedding and classification task.
1 code implementation • 16 Apr 2021 • Hao Peng, Ruitong Zhang, Yingtong Dou, Renyu Yang, Jingyi Zhang, Philip S. Yu
To avoid the embedding over-assimilation among different types of nodes, we employ a label-aware neural similarity measure to ascertain the most similar neighbors based on node attributes.
Ranked #4 on
Node Classification
on Amazon-Fraud
1 code implementation • 2 Apr 2021 • Hao Peng, JianXin Li, Yangqiu Song, Renyu Yang, Rajiv Ranjan, Philip S. Yu, Lifang He
Third, we propose a streaming social event detection and evolution discovery framework for HINs based on meta-path similarity search, historical information about meta-paths, and heterogeneous DBSCAN clustering method.
1 code implementation • 12 Aug 2020 • Hao Peng, Jian-Xin Li, Zheng Wang, Renyu Yang, Mingzhe Liu, Mingming Zhang, Philip S. Yu, Lifang He
As a departure from prior work, Luce organizes the house data in a heterogeneous information network (HIN) where graph nodes are house entities and attributes that are important for house price valuation.
2 code implementations • 2 Aug 2020 • Qian Li, Hao Peng, Jian-Xin Li, Congying Xia, Renyu Yang, Lichao Sun, Philip S. Yu, Lifang He
The last decade has seen a surge of research in this area due to the unprecedented success of deep learning.
no code implementations • 11 Oct 2019 • Bin Qian, Jie Su, Zhenyu Wen, Devki Nandan Jha, Yinhao Li, Yu Guan, Deepak Puthal, Philip James, Renyu Yang, Albert Y. Zomaya, Omer Rana, Lizhe Wang, Maciej Koutny, Rajiv Ranjan
Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock complete potentials of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing results to improve their business processes and services.