Large-scale Mobile Social Networks Research Articles

A Computational Model for Measuring Trust in Mobile Social Networks 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 systems 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.

Location Based Handshake and Private Proximity Test with Location Tags

A location proximity test service allows mobile users to determine whether they are in close proximity to each other, and has found numerous applications in mobile social networks. Unfortunately, existing solutions usually reveal much of users’ private location information during a proximity test. They are also vulnerable to location cheating where an attacker reports false locations to gain an advantage. Moreover, the initial trust establishment among unfamiliar users in large scale mobile social networks has been a challenging task. In this paper, we propose a novel scheme that enables a user to perform (1) a location based handshake that establishes secure communications among strangers, who do not have a pre-shared secret, and (2) a privacy-preserving proximity test without revealing the user’s actual location to the server or other users not within the proximity. The proposed scheme is based on a novel concept, i.e., spatial-temporal location tags, and we put forward a location tag construction method using environmental signals that provides an unforgeable location proof. We use Bloom filters to efficiently represent users’ location tags and vicinity regions. We exploit fuzzy extractor, a lightweight cryptographic primitive, to extract shared secrets between matching location tags. We conduct extensive analysis, simulation, and real experiments to demonstrate the feasibility, security, and efficiency of our scheme.

Towards Information Diffusion in Mobile Social Networks

The emerging of mobile social networks opens opportunities for viral marketing. However, before fully utilizing mobile social networks as a platform for viral marketing, many challenges have to be addressed. In this paper, we address the problem of identifying a small number of individuals through whom the information can be diffused to the network as soon as possible, referred to as the diffusion minimization problem. Diffusion minimization under the probabilistic diffusion model can be formulated as an asymmetric $k$ -center problem which is NP-hard, and the best known approximation algorithm for the asymmetric $k$ -center problem has approximation ratio of $\log ^*n$ and time complexity $O(n^5)$ . Clearly, the performance and the time complexity of the approximation algorithm are not satisfiable in large-scale mobile social networks. To deal with this problem, we propose a community based algorithm and a distributed set-cover algorithm. The performance of the proposed algorithms is evaluated by extensive experiments on both synthetic networks and a real trace. The results show that the community based algorithm has the best performance in both synthetic networks and the real trace compared to existing algorithms, and the distributed set-cover algorithm outperforms the approximation algorithm in the real trace in terms of diffusion time.

Influence Maximization on Mobile Social Network using Location based Community Greedy Algorithm

A mobile social network plays an important role as the spread of information and influence in the form of “word-of-mouth”. It is basic thing to find small set of influential people in a mobile social network such that targeting them initially. It will increase the spread of the influence .The problem of finding the most influential nodes in network is NP-hard. It has been shown that a Greedy algorithm with provable approximation guarantees can give good approximation. Community based Greedy algorithm is used for mining top-K influential nodes. It has two components: dividing the mobile social network into several communities by taking into account information diffusion and selecting communities to find influential nodes by a dynamic programming. Location Based community Greedy algorithm is used to find the influence node based on Location and consider the influence propagation within Particular area. Experiments result on real large-scale mobile social networks show that the proposed location based greedy algorithm has higher efficiency than previous community greedy algorithm. General Terms Mobile social network, Influence maximization, community greedy algorithm, Location based community greedy algotithm.

Influence Maximization on Large-Scale Mobile Social Network: A Divide-and-Conquer Method

With the proliferation of mobile devices and wireless technologies, mobile social network systems are increasingly available. A mobile social network plays an essential role as the spread of information and influence in the form of “word-of-mouth”. It is a fundamental issue to find a subset of influential individuals in a mobile social network such that targeting them initially (e.g., to adopt a new product) will maximize the spread of the influence (further adoptions of the new product). The problem of finding the most influential nodes is unfortunately NP-hard. It has been shown that a Greedy algorithm with provable approximation guarantees can give good approximation; However, it is computationally expensive, if not prohibitive, to run the greedy algorithm on a large mobile social network. In this paper, a divide-and-conquer strategy with parallel computing mechanism has been adopted. We first propose an algorithm called Community-based Greedy algorithm for mining top-K influential nodes. It encompasses two components: dividing the large-scale mobile social network into several communities by taking into account information diffusion and selecting communities to find influential nodes by a dynamic programming. Then, to further improve the performance, we parallelize the influence propagation based on communities and consider the influence propagation crossing communities. Also, we give precision analysis to show approximation guarantees of our models. Experiments on real large-scale mobile social networks show that the proposed methods are much faster than previous algorithms, meanwhile, with high accuracy.

Social based throwbox placement schemes for large-scale mobile social delay tolerant networks

Mobile phone sensing is a new paradigm which takes advantage of smart phones to collect and analyze data at large scale but with a low cost. Supporting pervasive communications among mobile devices in such a large-scale mobile social network becomes a key challenge for this new mobile sensing system. One possible solution is allowing packet delivery among mobile devices via opportunistic communications during intermittent contacts. However, the lack of rich contact opportunities still causes poor delivery ratio and long delay, especially for large-scale networks. Deployment of additional stationary throwboxes can create a greater number of contact opportunities, thus improve the performance of routing. However, the locations of deployed throwboxes are critical to such improvement. In this paper, we investigate where to deploy throwboxes in a large-scale throwbox-assisted mobile social DTN. By leveraging the social properties discovered from real-life tracing data, we propose a set of social-based throwbox placement algorithms which smartly pick the location of each throwbox. Extensive simulations are conducted with a real-life wireless tracing dataset and a wide range of existing DTN routing methods. The results confirm the efficiency of the proposed methods.

A Greedy Algorithm Approach for Mobile Social Network

With the proliferation of mobile devices and wireless technologies, mobile social network systems used more. A mobile social network has important role in social network. The Process of finding influential nodes is NP-hard. Greedy rule with demonstrable approximation guarantees will provide smart approximation. A divide-and-conquer method with parallel computing mechanism has been used. Communitybased Greedy rule for mining top-K influential nodes is used first. It has two parts: dividing the largescale mobile social network into many communities by taking under consideration data diffusion. Communities select influential nodes by a dynamic programming. Performance is to be increased by considering the influence propagation supported communities and take into account the influence propagation crossing communities. Experiments on real large-scale mobile social networks show that the proposed algorithm is quicker than previous algorithms. General Terms Mobile social network, influence maximization, PCA greedy algorithm,

κ-FuzzyTrust: Efficient trust computation for large-scale mobile social networks using a fuzzy implicit social graph

Large-scale mobile social networks (MSNs) facilitate connections between mobile devices and provide an effective mobile computing environment in which users can access, share, and distribute information. In MSNs, users may belong to more than one community or cluster, and overlapping users may play a special role in complex MSNs. For such MSNs, a key problem is how to evaluate or explain user trustworthiness. In this context, trust inference plays a critical role in establishing trusted social links between mobile users. To infer fuzzy trust relations between users in MSNs with overlapping communities, we propose an efficient trust inference mechanism based on fuzzy communities, which we call κ-FuzzyTrust. We propose an algorithm for detection of community structure in complex networks under fuzzy degree κ and construct a fuzzy implicit social graph. We then construct a mobile social context including static attributes (such as user profile and prestige) and dynamic behavioural characteristics(such as user interaction partners, interaction familiarity, communication location and time) based on the fuzzy implicit social graph. We infer the trust value between two mobile users using this mobile social context. We discuss the aggregation and propagation of trust values for overlapping users and indirect connected users. Finally, we evaluate the performance of κ-FuzzyTrust in simulations. The results show the validity of our fuzzy inference mechanism for behavioural trust relationships in MSNs. They also demonstrate that κ-FuzzyTrust can infer trust values with high precision.