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| 1 | A survey of social network and information dissemination analysis显示文摘In recent years,with the rapid growth of social network services(SNS),social networks pervade nearly every aspect of our daily lives.Social networks are influencing today’s societal and cultural issues,and changing the way of people seeing themselves.To fully understand the running mechanisms of social networks,in this paper,we aim at series of high knitted and important elements of online social networks.We mainly focus on 3 important but also open research problems,they are(1)structural properties and evolving laws,(2)social crowds and their interaction behaviors and(3)information and its diffusion.In this paper,we review the related work on the 3 problems.Then,we briefly introduce some interesting research directions and our progress on these research problems. | Binxing Fang Yan Jia Yi Han Shasha Li Bin Zhou | 2014 | Chinese Science Bulletin2014，59，32： | 7 |
| 2 | Analysis of Monosaccharide Composition of Pu erh Tea Polysaccaride by Pre column Derivatization HPLC 显示文摘 | Wei GUO Binxing ZHOU Ling LUO | 2013 | Agricultur- al Science & Technology2013，14，55： | 1 |
| 3 | Ripple+:An Improved Scheme of Ripple Consensus Protocol in Deployability,Liveness and Timing Assumption显示文摘Ripple acts as a real-time settlement and payment system to connect banks and payment providers.As the consensus support of the Ripple network to ensure network consistency,Ripple consensus protocol has been widely concerned in recent years.Compared with those Byzantine fault tolerant protocols,Ripple has a significant difference that the system can reach an agreement under decentralized trust model.However,Ripple has many problems both in theory and practice,which arementioned in the previous researches.This paper presents Ripple+,an improved scheme of Ripple consensus protocol,which improves Ripple fromthree aspects:(1)Ripple+employs a specific trustmodel and a corresponding guideline for Unique Node List selection,which makes it easy to deploy in practice to meet the safety and liveness condition;(2)the primary and viewchangemechanismare joined to solve the problem discussed by the previous research that Ripple may lose liveness in some extreme scenarios;(3)we remove the strong synchrony clock and timeout during consensus periods to make it suitable for weak synchrony assumption.We implemented a prototype of Ripple+and conducted experiments to show that Ripple+can achieve the throughput of tens of thousands of transactions per second with no more than half a minute latency,and the view change mechanism hardly incurs additional cost. | Chuanwang Ma Yu Zhang Binxing Fang Hongli Zhang Yidong Jin Dasheng Zhou | 2022 | Computer Modeling in Engineering & Sciences2022，，1： | 1 |
| 4 | Research on the Extraction Technology of Pu-erh Tea Polysaccharide by Response Surface Analysis显示文摘 | Luo Ling Zhou Binxing Guo Wei | | 0，，： | 1 |
| 5 | Modeling Multi-factor Sequential User Behavior Data over Social Networks显示文摘Modeling dynamic user behavior over online social networks not only helps us understand user behavior patterns on social networks, but also improves the performance of behavior analysis tasks. Time-varying user behavior is commonly influenced by multiple factors: user habit, social influence and external events. Existing works either consider only a part of these factors, or fail to model the dynamics behind user behavior. Thus, they cannot precisely model the user behavior. We present a generative Bayesian model HES to model dynamic user behavior data.We take the influential factors and user's selection process as separate latent variables, based on which we can recover the evolving patterns underneath user behavior data sequences. Empirical results on large-scale social networks show that the proposed approach outperforms existing user behavior prediction models by at least 8% w.r.t. prediction accuracy. Our work also unveils some interesting insights underneath social behavior data. | WANG Peng ZHANG Peng ZHOU Chuan GUO Li FANG Binxing YANG Tao | 2016 | Chinese Journal of Electronics2016，25，2： | 0 |
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