Recommending Friends Instantly in Location-based Mobile Social Networks

Differently from the general online social network(OSN),locationbased mobile social network(LMSN),which seamlessly integrates mobile computing and social computing technologies,has unique characteristics of temporal,spatial and social correlation.Recommending friends instantly based on current location of users in the real world has become increasingly popular in LMSN.However,the existing friend recommendation methods based on topological structures of a social network or non-topological information such as similar user profiles cannot well address the instant making friends in the real world.In this article,we analyze users' check-in behavior in a real LMSN site named Gowalla.According to this analysis,we present an approach of recommending friends instantly for LMSN users by considering the real-time physical location proximity,offline behavior similarity and friendship network information in the virtual community simultaneously.This approach effectively bridges the gap between the offline behavior of users in the real world and online friendship network information in the virtual community.Finally,we use the real user check-in dataset of Gowalla to verify the effectiveness of our approach.

APPOW:An Advanced Routing Protocol Based on Parameters Optimization in the Weighted Mobile Social Network

Due to the increasing number of wireless mobile devices,the possibility of mobile communications without infrastructure becomes a reality.The Decentralized Mobile Social Network(DMSN) is a paradigm where nodes can move freely and organize themselves arbitrarily.Routing in these environments is difficult for the reason of the rapid changes of the social relationship graph's topology.Meanwhile,the social ties among nodes change overtime.Therefore,an efficient data forwarding mechanism should be considered over the temporal weighted relationship graph.In this paper,an Advanced routing Protocol based on Parameters Optimization in the Weighted mobile social network(APPOW) is proposed to improve the delivery success ratio and reduce the cost of exchanging information.APPOW combines the normalized relative weights of three local social metrics,i.e.,LinkRank,similarity and contact strength,to select the next relay node.The weights of the three metrics are derived by pair-wise learning algorithm.The result shows that APPOW outperforms the state-ofthe-art SimBet Routing in delivering message and significantly reduces the average hops.Additionally,the delivery performance of APPOW is close to Epidemic Routing but without message duplications.

Community Discovery with Location-Interaction Disparity in Mobile Social Networks

With the fast-growth of mobile social network, people＇ s interactions are frequently marked with location information, such as longitude and latitude of visited base station. This boom of data has led to considerable interest in research fields such as user behavior mining, trajectory discovery and social demographics. However, there is little research on community discovery in mobile social networks, and this is the problem this work tackles with. In this work, we take advantage of one simple property that people in different locations often belong to different social circles in order to discover communities in these networks. Based on this property, which we referred to as Location-lnteraction Disparity （LID）, we proposed a state network and then define a quality function evaluating community detection results. We also propose a hybrid community- detection algorithm using LID tor discovering location-based communities effectively and efficiently. Experiments on synthesis networks show that this algorithm can run effectively in time and discover communities with high precision. In realworld networks, the method reveals people＇s different social circles in different places with high efficiency.

Research on the Association of Mobile Social Network Users Privacy Information Based on Big Data Analysis

The issue of privacy protection for mobile social networks is a frontier topic in the field of social network applications.The existing researches on user privacy protection in mobile social network mainly focus on privacy preserving data publishing and access control.There is little research on the association of user privacy information,so it is not easy to design personalized privacy protection strategy,but also increase the complexity of user privacy settings.Therefore,this paper concentrates on the association of user privacy information taking big data analysis tools,so as to provide data support for personalized privacy protection strategy design.

Fractal Analysis of Mobile Social Networks

Fractal and self similarity of complex networks have attracted much attention in recent years. The fractal dimension is a useful method to describe the fractal property of networks. However, the fractal features of mobile social networks （MSNs） are inadequately investigated. In this work, a box-covering method based on the ratio of excluded mass to closeness centrality is presented to investigate the fractal feature of MSNs. Using this method, we find that some MSNs are fractal at different time intervals. Our simulation results indicate that the proposed method is available for analyzing the fractal property of MSNs.

Personalized Privacy Protecting Model in Mobile Social Network

With the rapid development of the new generation of information technology,the analysis of mobile social network big data is getting deeper and deeper.At the same time,the risk of privacy disclosure in social network is also very obvious.In this paper,we summarize the main access control model in mobile social network,analyze their contribution and point out their disadvantages.On this basis,a practical privacy policy is defined through authorization model supporting personalized privacy preferences.Experiments have been conducted on synthetic data sets.The result shows that the proposed privacy protecting model could improve the security of the mobile social network while keeping high execution efficiency.

RELOCATION ALGORITHM FOR NON-UNIFORM DISTRIBUTION IN MOBILE SENSOR NETWORK

Energy is the determinant factor for the survival of Mobile Sensor Networks(MSN).Based on the analysis of the energy distribution in this paper,a two-phase relocation algorithm is proposed based on the balance between the energy provision and energy consumption distribution.Our main objectives are to maximize the coverage percentage and to minimize the total distance of node movements.This algorithm is designed to meet the requirement of non-uniform distribution network applications,to extend the lifetime of MSN and to simplify the design of the routing protocol.In ad-dition,test results show the feasibility of our proposed relocation algorithm.

Security Threat and Data Consumption as Mojor Nuisance of Social Media on Wi-Fi Network

This research is about the nuisances of social media applications on a Wi-Fi network at a university campus in Ghana. The aim was to access the security risk on the network, the speed of the network, and the data consumption of those platforms on the network. Network Mapper (Nmap Zenmap) Graphical User Interface 7.80 application was used to scan the various social media platforms to identify the protocols, ports, services, etc. to enable in accessing the vulnerability of the network. Data consumption of users’ mobile devices was collected and analyzed. Device Accounting (DA) based on the various social media applications was used. The results of the analysis revealed that the network is prone to attacks due to the nature of the protocols, ports, and services on social media applications. The numerous users with average monthly data consumption per user of 4 gigabytes, 300 megabytes on social media alone are a clear indication of high traffic as well as the cost of maintaining the network. A URL filtering of the social media websites was proposed on Rockus Outdoor AP to help curb the nuisance.

Mobile Social Net work——Research and Implementation of Mobile Twitter System

This paper researched and analyzedweb2.0 technology and mobile social network.Then researched and implemented the mobile twitter system.This paper introduces the function and modules of mobile client and PC server respectively.We also had the user experience and system test which are wrote in this paper.

沟通，从“朋友圈”开始——UUFirend，引爆Social Network服务新亮点

前不久，众多网络媒体相继报道了一种能将即时消息、Blog和社会网络融为一体的、新型的Social Software（社会性软件）——UUFriend成功发布并已经开始了试运营。

MSNs中基于TCC感知的数据转发方法

现有的移动社交网络数据转发方法大多没有考虑到处存在的暂态连通区域(TCC),导致数据转发质量较低。为此,根据5组真实数据对TCC属性与数据转发性能的关系进行分析,提出一种基于TCC感知的数据转发算法。考虑到该算法会增加网络中数据拷贝的数量,提出改进型TCC感知数据转发策略,通过选择TCC中的最优节点集,避免它们的相遇重叠现象,利用少量节点实现数据转发机会最大化。仿真结果表明,所提方法的性能在数据投递率和网络开销方面的性能优于当前其它数据转发算法。

BIRI:支持信息中心范型的BBO启发式MSN路由算法

智能移动终端的普及大大推动了移动社交网络(mobile social networks, MSNs)的发展.人类作为终端设备的载体具备频繁的移动性,导致网络拓扑的动态变化,并给MSN路由带来了时延长、投递率低、开销大等诸多难题.为提升路由效率,基于信息中心网络(information centric networking, ICN)以内容为中心的思想以及生物地理优化(biogeography-based optimization, BBO)算法,设计了一种高效的支持信息中心范型的BBO启发式MSN路由算法(BBO-inspired MSN routing algorithm with information-centric paradigm support, BIRI).首先,该机制基于重定义的社交度量——社会关系强度和中心度——使用BBO算法进行社区划分.其次,设计了内容聚集、数据缓存以及桥节点选取策略,支持高效的内容检索和访问.基于上述策略,提出了优化的社区间和社区内路由过程,缓解终端移动性对数据传输带来的影响.在机会网络环境(opportunistic network environment, ONE)中,仿真实现BIRI机制,并且与其他3种MSN路由机制从投递率、平均时延、网络开销比率3个指标进行性能对比与分析,实验结果表明BIRI是一种可行且高效的MSN路由机制.

基于ICN网络架构的社区感知型MSN路由机制

移动社交网络(mobile social network,简称MSN)利用移动用户之间的社交关系,通过节点间的协作式转发实现消息交付.然而,随着大数据时代的到来,MSN需要满足移动用户日益增长的对内容(如视频)的需求.由于信息中心网络(information-centric networking,简称ICN)对移动性的支持,基于ICN架构,提出了一种MSN中基于社区划分的路由机制.在兴趣决策中,利用节点请求中的内容名字获取用户的兴趣偏好,进而计算用户间的兴趣差异度量;根据兴趣差异将节点划分为兴趣社区,依据这些兴趣社区进行兴趣包路由.在数据决策中,根据节点历史相遇信息计算用户间的相遇规律度量,根据相遇规律将节点划分为社交社区,依据这些社交社区进行数据包路由.同时,根据兴趣社区和社交社区信息优化节点的内容缓存,以快速满足未来的内容请求.进行了仿真实验,通过与现有机制在包交付率、平均延迟、平均跳数和网络开销方面的性能对比,表明所提出的机制是可行且有效的.

基于朋友圈和节点感知的内容中心MSN路由机制

为了解决移动社交网络(MSN)中由于网络拓扑的时变性以及节点资源的受限性导致路由时延长、投递率低等诸多难题,利用信息中心网络(ICN)内容和位置分离的特点更好地支持终端移动性和网内缓存,设计了一种基于朋友圈和节点感知的内容中心MSN路由机制(FACMR)。首先,明确了节点模型的功能表,使节点具有感知能力。然后,基于节点关系强度和其缓存内容相似性构造朋友圈,快速高效地指导路由。其次,利用缓存特征和置换策略进行朋友圈的维护。最后,利用网络模拟器(NS3)对FACMR进行仿真。仿真结果表明,FACMR与现有的典型算法相比,缓存置换率减少了6.35%,误包数减少了22.9%,平均路由时延降低了43.5%,是一种可行且高效的MSN路由机制。

UWB技术在DT-MSNs中的应用

容延迟移动传感器网络(DT-MSNs)是将容延迟技术引入到无线传感器网络(W SNs)而产生的新兴网络模型。它具有节点移动,网络密度稀疏、非持续连接等特点。针对DT-MSNs方法这些特点,分析了其底层基于传统窄带通信所面临的问题,提出了将超宽带(UWB)技术应用到DT-MSNs中的想法,利用UWB的高传输速率、低能耗、高抗干扰能力,提高DT-MSN中的数据传输能力,改善网络性能。采用DCC-MAC协议到已有的DT-MSNs仿真实验平台,通过仿真实验证明:将UWB技术应用在DT-MSNs中是可行的。

一种基于DT-MSN的性能预评估方法

为提高网络的利用率,解决DT-MSN中网络部署问题,运用子图的扩展理论,给出一种判定是否存在和全网同构的样本网络方法。在存在样本网络的前提下,用最小生成树原理提出一种基于节点间最大连接次数的节点分簇算法。该算法可以构建一种样本网络,在投递的数据流上和全网同构。构建一个样本网络场景,并和全网作实验对比分析。结果表明,构建样本网络模型可以代替全网做性能的预评估,能为部署大型网络场景提供可靠的性能评估报告,具有较好的实际应用价值。

Socialization, Traffic Distribution and E-Commerce Trends:An Interpretation of the “Pinduoduo” Phenomenon

The article takes China’s e-commerce as the research object.Starting from the macro level of e-commerce development and taking the rapid rise of“Pinduoduo”as an example,it discusses the“traffic dilemma”and its influence in the traditional e-commerce platform.This discovers the internal mechanism of mobile e-commerce to solve the problem of traffic distribution mechanism by socialization.After that,this study compares the difference between traditional e-commerce and social-commerce systematically,and concludes that traditional e-commerce platform is a necessary process of the development of social-commerce.Socialization is an important trend of the development of traditional e-commerce and social-commerce will promote the realization of C2B model.

Seed Selection for Data Offloading Based on Social and Interest Graphs

The explosive growth of mobile data demand is becoming an increasing burden on current cellular network.To address this issue,we propose a solution of opportunistic data offloading for alleviating overloaded cellular traffic.The principle behind it is to select a few important users as seeds for data sharing.The three critical steps are detailed as follows.We first explore individual interests of users by the construction of user profiles,on which an interest graph is built by Gaussian graphical modeling.We then apply the extreme value theory to threshold the encounter duration of user pairs.So,a contact graph is generated to indicate the social relationships of users.Moreover,a contact-interest graph is developed on the basis of the social ties and individual interests of users.Corresponding on different graphs,three strategies are finally proposed for seed selection in an aim to maximize overloaded cellular data.We evaluate the performance of our algorithms by the trace data of real-word mobility.It demonstrates the effectiveness of the strategy of taking social relationships and individual interests into account.