Zobrazeno 1 - 10
of 126
pro vyhledávání: '"Wang, Huandong"'
Learning complex network dynamics is fundamental for understanding, modeling, and controlling real-world complex systems. Though great efforts have been made to predict the future states of nodes on networks, the capability of capturing long-term dyn
Externí odkaz:
http://arxiv.org/abs/2408.09845
The increasing parameters and expansive dataset of large language models (LLMs) highlight the urgent demand for a technical solution to audit the underlying privacy risks and copyright issues associated with LLMs. Existing studies have partially addr
Externí odkaz:
http://arxiv.org/abs/2408.08661
Generating human mobility trajectories is of great importance to solve the lack of large-scale trajectory data in numerous applications, which is caused by privacy concerns. However, existing mobility trajectory generation methods still require real-
Externí odkaz:
http://arxiv.org/abs/2407.16729
Autor:
Ding, Jingtao, Liu, Chang, Zheng, Yu, Zhang, Yunke, Yu, Zihan, Li, Ruikun, Chen, Hongyi, Piao, Jinghua, Wang, Huandong, Liu, Jiazhen, Li, Yong
Complex networks pervade various real-world systems, from the natural environment to human societies. The essence of these networks is in their ability to transition and evolve from microscopic disorder-where network topology and node dynamics intert
Externí odkaz:
http://arxiv.org/abs/2402.16887
Membership Inference Attacks (MIA) aim to infer whether a target data record has been utilized for model training or not. Prior attempts have quantified the privacy risks of language models (LMs) via MIAs, but there is still no consensus on whether e
Externí odkaz:
http://arxiv.org/abs/2311.06062
Publikováno v:
ACM Trans. Inf. Syst. 42 (2024) 1 - 29
Accurately predicting individual-level infection state is of great value since its essential role in reducing the damage of the epidemic. However, there exists an inescapable risk of privacy leakage in the fine-grained user mobility trajectories requ
Externí odkaz:
http://arxiv.org/abs/2311.06049
Membership Inference Attack (MIA) identifies whether a record exists in a machine learning model's training set by querying the model. MIAs on the classic classification models have been well-studied, and recent works have started to explore how to t
Externí odkaz:
http://arxiv.org/abs/2308.12143
Autor:
Gao, Chen, Lan, Xiaochong, Lu, Zhihong, Mao, Jinzhu, Piao, Jinghua, Wang, Huandong, Jin, Depeng, Li, Yong
Social network simulation plays a crucial role in addressing various challenges within social science. It offers extensive applications such as state prediction, phenomena explanation, and policy-making support, among others. In this work, we harness
Externí odkaz:
http://arxiv.org/abs/2307.14984
Understanding and characterizing the vulnerability of urban infrastructures, which refers to the engineering facilities essential for the regular running of cities and that exist naturally in the form of networks, is of great value to us. Potential a
Externí odkaz:
http://arxiv.org/abs/2307.09866
Autor:
Wang, Huandong, Yan, Huan, Rong, Can, Yuan, Yuan, Jiang, Fenyu, Han, Zhenyu, Sui, Hongjie, Jin, Depeng, Li, Yong
Complex system simulation has been playing an irreplaceable role in understanding, predicting, and controlling diverse complex systems. In the past few decades, the multi-scale simulation technique has drawn increasing attention for its remarkable ab
Externí odkaz:
http://arxiv.org/abs/2306.10275