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.

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.

Dynamic coverage of mobile multi-target in sensor networks based on virtual force

A distributed self-control coverage method for mobile multi-target based on virtual force(MMTVF)is proposed to monitor dynamic targets using a mobile sensor network(MSN).The dynamic coverage method is introduced to maintain network connectivity and optimize the coverage of moving targets.The method consists of two parts,one is the virtual force model which is proposed for motion control,and the other is the whale optimization algorithm which is improved to optimize node positions and achieve a steady state quickly.The virtual resultant force stretches the network towards uncovered targets using its multi-target attractive force,maintains network connectivity during network stretching using its attractive force,and prevents node collisions while nodes are moving using its repulsive force.The operating mechanism of the multi-target attractive force and other forces is thoroughly analyzed.Adjustment criteria for the model in different application scenarios are also provided.The comparisons demonstrate MMTVF has significant advantages over other similar approaches.

User Profile System Based on Sentiment Analysis for Mobile Edge Computing

Emotions of users do not converge in a single application but are scattered across diverse applications.Mobile devices are the closest media for handling user data and these devices have the advantage of integrating private user information and emotions spread over different applications.In this paper,we first analyze user profile on a mobile device by describing the problem of the user sentiment profile system in terms of data granularity,media diversity,and server-side solution.Fine-grained data requires additional data and structural analysis in mobile devices.Media diversity requires standard parameters to integrate user data from various applications.A server-side solution presents a potential risk when handling individual privacy information.Therefore,in order to overcome these problems,we propose a general-purposed user profile system based on sentiment analysis that extracts individual emotional preferences by comparing the difference between public and individual data based on particular features.The proposed system is built based on a sentiment hierarchy,which is created by using unstructured data on mobile devices.It can compensate for the concentration of single media,and analyze individual private data without the invasion of privacy on mobile devices.

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 Applications Provisioning Using Bluetooth Wireless Technology

In this paper, we propose a cheap means for propagating mobile application using Bluetooth, a convenient short range wireless technology. Today, one of the main problems about mobile technology is about implementing automatically the software in the multiple and various type of phones devices, irrespective of the phone model (Nokia, Samsung, iPhone, etc.). There are many ways of doing OTA like by SMS. This approach allows providing an http download link by SMS but, we are limited by the maximum size one can have to build an application and send it trough this means. This approach is also expensive because data sources are expensive. Within the context of mobile social networking and proximity marketing, we come up with an original way of provisioning mobile applications using Bluetooth. We use anybody who is already a subscriber of an application to become an ambassador of it. The application is self-replicating and distributing in itself and sending back results. It ethically acts like a virus or a disease. A prototype is built to validate the proposed methodology.

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。

基于区块链的用户自定义位置共享方案

针对移动社交网络位置共享服务存在的隐私泄漏问题,提出基于区块链的用户自定义位置共享(BUDLS)方案.基于区块链实现位置信息分布式管理,防止中央服务器收集大量用户隐私,增强用户位置信息的可控性.设计基于公钥数字签名和同态加密相结合的加密机制,防止位置信息被攻击者非法获取.定义灵活的访问控制策略,根据用户需要提供可靠的服务.安全分析验证结果显示,BUDLS方案满足隐私安全目标.仿真实验结果表明,相比传统方案,BUDLS方案降低了时间成本,提高了位置查询的准确性,有效保护了移动社交网络平台用户的位置隐私.

面向强稀疏性移动社交网络的链路预测深度学习方法

链路预测是利用深度学习技术分析网络数据,挖掘网络中潜在的节点关系,通常应用于网络安全、信息挖掘等领域。通过预测网络中节点间的链路,可以识别社交工程攻击、欺诈行为和隐私泄露风险。但移动社交网络的拓扑结构随时间变化,链路稀疏,影响预测准确性。为了解决移动社交网络中链路预测的强稀疏性问题,提出基于深度学习的预测方法,即面向强稀疏性移动社交网络的链路预测深度学习方法(deep learning-based method for mobile social networks with strong sparsity for link prediction,DLMSS-LP)。该方法综合运用了图自编码器(graph auto-encoder,GAE)、特征矩阵聚合技术以及多层长短期记忆网络(long short-term memory,LSTM),旨在降低了模型的学习成本,更有效地处理高维和非线性的网络结构,并且捕捉移动社交网络中的时序动态变化,进而增强模型对现有链路生成可能性的预测能力。对比其他方法在AUC(area under curve)和ER(error rate)指标上有明显提升,体现了模型对不确定链路预测的高准确率和强鲁棒性。

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。

移动社交网络用户的浏览隐私保护

为保护用户在社交网络中浏览信息的安全,设计移动社交网络用户浏览隐私保护方法。先设置用户浏览隐私度量标准,计算用户浏览信息轨迹斜率,获得不同阶段用户浏览信息斜率比,再结合斜率夹角,得到轨迹函数。基于人工智能技术建立隐私信息分类模型,计算发起者与普通用户之间的相似性,确定用户被正确识别的概率,获取信息分类模型分类精度。设计社交网络用户浏览隐私保护算法,计算虚假轨迹区域的轨迹数目,定义圆心位置,获取移动社交网络用户的隐私匿名保护结果。实验结果显示,该隐私保护方法在隐私保护度、匿名时延和算法匿名开销等方面均有较高性能。

甘肃省高层次科技人才流动复杂网络特征研究

文章通过履历分析法对2000年以来甘肃省高层次科技人才流动信息进行挖掘,然后根据人才流动信息构建流动网络,最后采用社会网络分析法对甘肃省高层次科技人才流动复杂网络结构特征进行研究。结果表明,甘肃省高层次科技人才跨国流动主要集中在美国、德国、日本等发达国家;跨省流动则主要集中在北京、四川、陕西等省份。通过分析流动网络核心-边缘结构发现,美国、德国和英国处于跨国流动网络核心位置,北京、上海、陕西和四川处于跨省流动网络核心位置。