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.

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.

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.

Cellular traffic offloading utilizing set-cover based caching in mobile social networks

To cope with the explosive data demands, offloading cellular traffic through mobile social networks（MSNs） has become a promising approach to alleviate traffic load. Indeed, the repeated data transmission results in a great deal of unnecessary traffic. Existing solutions generally adopt proactive caching and achieve traffic shifting by exploiting opportunistic contacts. The key challenge to maximize the offloading utility needs leveraging the trade-off between the offloaded traffic and the users＇ delay requirement. Since current caching scheme rarely address this challenge, in this paper, we first quantitatively interpret the offloading revenues on the cellular operator side associated with the scale of caching users, then develop a centralized caching protocol to maximize the offloading revenues, which includes the selective algorithm of caching location based on set-cover, the cached-data dissemination strategy based on multi-path routing and the cache replacement policy based on data popularity. The experimental results on real-world mobility traces show that the proposed caching protocol outperforms existing schemes in offloading scenario.

Discovering Typed Communities in Mobile Social Networks

Mobile social networks, which consist of mobile users who communicate with each other using cell phones are reflections of people＇s interactions in social lives. Discovering typed communities （e.g., family communities or corporate communities） in mobile social networks is a very promising problem. For example, it can help mobile operators to determine the target users for precision marketing. In this paper we propose discovering typed communities in mobile social networks by utilizing the labels of relationships between users. We use the user logs stored by mobile operators, including communication and user movement records, to collectively label all the relationships in a network, by employing an undirected probabilistic graphical model, i.e., conditional random fields. Then we use two methods to discover typed communities based on the results of relationship labeling： one is simply retaining or cutting relationships according to their labels, and the other is using sophisticated weighted community detection algorithms. The experimental results show that our proposed framework performs well in terms of the accuracy of typed community detection in mobile social networks.

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.

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.

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.

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.

Energy Efficient Social Routing Framework for Mobile Social Sensing Networks

Mobile social sensing network is one kind of emerging networks in which sensing tasks are performed by mobile users and sensing data are shared and collected by leveraging the intermittent inter-contacts among mobile users. Traditional ad hoc routing protocols are inapplicable or perform poorly for data collection or data sharing in such mobile social networks because nodes are seldom fully connected. In recent years, many routing protocols （especially social-based routing） are proposed to improve the delivery ratio in mobile social networks, but most of them do not consider the load of nodes thus may lead to unbalanced energy consumption among nodes. In this paper, we propose a simple Energy Efficient framework for Social-based Routing （EE-SR） in mobile social sensing networks to balance the load of nodes while maintaining the delivery ratio within an acceptable range by limiting the chances of forwarding in traditional social-based routing. Furthermore, we also propose an improved version of EE-SR to dynamically adjust the controlling parameter. Simulation results on real-life mobile traces demonstrate the efficiency of our proposed framework.

A Computational Model for Measuring Trust in Mobile Social Net works Using Fuzzy Logic

Large-scale mobile social networks(MSNs)facilitate communications through mobile devices.The users of these networks can use mobile devices to access,share and distribute information.With the increasing number of users on social networks,the large volume of shared information and its propagation has created challenges for users.One of these challenges is whether users can trust one another.Trust can play an important role in users'decision making in social networks,so that,most people share their information based on their trust on others,or make decisions by relying on information provided by other users.However,considering the subjective and perceptive nature of the concept of trust,the mapping of trust in a computational model is one of the important issues in computing systeins of social networks.Moreover,in social networks,various communities may exist regarding the relationships between users.These connections and communities can affect trust among users and its complexity.In this paper,using user characteristics on social networks,a fuzzy clustering method is proposed and the trust between users in a cluster is computed using a computational model.Moreover,through the processes of combination,transition and aggregation of trust,the trust value is calculated between users who are not directly connected.Results show the high performance of the proposed trust inference method.

Epidemic-Like Proximity-Based Traffic Offloading

Cellular networks are overloaded due to the mobile traffic surge,and mobile social networks(MSNets) can be leveraged for traffic offloading.In this paper,we study the issue of choosing seed users for maximizing the mobile traffic offloaded from cellular networks.We introduce a gossip-style social cascade(GSC) model to model the epidemic-like information diffusion process in MSNets.For static-case and mobile-case networks,we establish an equivalent view and a temporal mapping of the information diffusion process,respectively.We further prove the submodularity in the information diffusion and propose a greedy algorithm to choose the seed users for traffic offloading,yielding a sub-optimal solution to the NP-hard traffic offloading maximization(TOM) problem.Experiments are carried out to study the offloading performance,illustrating that the greedy algorithm significantly outperforms the heuristic and random algorithms,and user mobility can help further reduce cellular load.

Trust-Based Context-Aware Mobile Social Network Service Recommendation

The service recommendation mechanism as a key enabling technology that provides users with more proactive and personalized service is one of the important research topics in mobile social network （MSN）. Meanwhile, MSN is susceptible to various types of anonymous information or hacker actions. Trust can reduce the risk of interaction with unknown entities and prevent malicious attacks. In our paper, we present a trust-based service recommendation algorithm in MSN that considers users＇ similarity and friends＇ familiarity when computing trustworthy neighbors of target users. Firstly, we use the context information and the number of co-rated items to define users＇ similarity. Then, motivated by the theory of six degrees of space, the friend familiarity is derived by graph-based method. Thus the proposed methods are further enhanced by considering users＇ context in the recommendation phase. Finally, a set of simulations are conducted to evaluate the accuracy of the algorithm. The results show that the friend familiarity and user similarity can effectively improve the recommendation performance, and the friend familiarity contributes more than the user similarity.

Next POI Recommendation Based on Location Interest Mining with Recurrent Neural Networks

In mobile social networks,next point-of-interest(POI)recommendation is a very important function that can provide personalized location-based services for mobile users.In this paper,we propose a recurrent neural network(RNN)-based next POI recommendation approach that considers both the location interests of similar users and contextual information(such as time,current location,and friends’preferences).We develop a spatial-temporal topic model to describe users’location interest,based on which we form comprehensive feature representations of user interests and contextual information.We propose a supervised RNN learning prediction model for next POI recommendation.Experiments based on real-world dataset verify the accuracy and efficiency of the proposed approach,and achieve best F1-score of 0.196754 on the Gowalla dataset and 0.354592 on the Brightkite dataset.