S
Sancheng Peng
Researcher at Guangdong University of Foreign Studies
Publications - 52
Citations - 1341
Sancheng Peng is an academic researcher from Guangdong University of Foreign Studies. The author has contributed to research in topics: Computer science & Data collection. The author has an hindex of 15, co-authored 45 publications receiving 927 citations. Previous affiliations of Sancheng Peng include Zhaoqing University & Hunan University of Technology.
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Influence analysis in social networks: A survey
TL;DR: This survey aims to pave a comprehensive and solid starting ground for interested readers by soliciting the latest work in social influence analysis from different levels, such as its definition, properties, architecture, applications, and diffusion models.
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Smartphone Malware and Its Propagation Modeling: A Survey
Sancheng Peng,Shui Yu,Aimin Yang +2 more
TL;DR: In order to understand the propagation behavior of smartphone malware, generic epidemic models are recalled as a foundation for further exploration and the smartphone malware propagation models are extensively surveyed.
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Social influence modeling using information theory in mobile social networks
TL;DR: This paper presents an evaluation model to measure both direct and indirect influence based on the social relationship graph, by introducing friend entropy and interaction frequency entropy to describe the complexity and uncertainty of social influence.
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Social Influence Analysis in Social Networking Big Data: Opportunities and Challenges
TL;DR: The goal is to provide a broad research guideline of existing and ongoing efforts via social influence analysis in large-scale social networks, and to help researchers better understand the existing work, and design new algorithms and methods forsocial influence analysis.
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Privacy of Things: Emerging Challenges and Opportunities in Wireless Internet of Things
TL;DR: This article summarizes privacy constraints and primary attacks based on new features of IoT, and identifies three key challenges: a lack of theoretical foundation, the trade-off optimization between privacy and data utility, and system isomerism over-complexity and high scalability.