Nodes In Mobile Social Networks Research Articles

Introducing a new algorithm based on collaborative game theory with the power of learning selfish node records to encourage selfish nodes in mobile social networks

Introducing a new algorithm based on collaborative game theory with the power of learning selfish node records to encourage selfish nodes in mobile social networks

Finding Best Matching Community for Common Nodes in Mobile Social Networks

The increase of mobile data users has created traffic congestion in current cellular networks. Due to this, mobile network providers have been facing difficulty in delivering the best services for customers. Since, detecting community in mobile social network is a valuable technique to leverage the downlink traffic congestion by enhancing local communications within the community, it attracts the attention of many researchers. Therefore, developing an algorithm, which detects community, plays a key role in mobile social network. In this paper, first, we proposed external density metrics to detect mobile social network. External density is defined as the ratio of outgoing links to total links of the community. Second, method to find the best group for common node is proposed. Therefore, an external density algorithm, makes a fair partition by grouping common nodes to a community with relatively higher external density. As a result, the overall modularity value of the network has increased. Third, the proposed algorithm is evaluated. Hence, the evaluation results confirm that our proposed approach has demonstrated good performance improvements than traditional methods.

Vital nodes extracting method based on user's behavior in 5G mobile social networks

In mobile social networks, extracting the most powerful individuals to disseminate information in the network is attracting the attention of many researchers. Identifying influential nodes in Mobile Social Networks (MSNs) helps to increase the efficiency of bandwidth in wireless communication by leveraging cellular links to device-to-device communications. Recently, numerous techniques have been proposed from different perspectives, each with its particular advantages and weaknesses. In this paper, Node Willingness and Influence (NWI) algorithm is proposed to extract the most powerful spreaders in 5G MSNs, which considers the willingness and influence of the node to disseminate information in the network through its neighbors and 2-step neighbors. Firstly, the Influence (In) of a node is calculated based on the nodes' willingness to share contents with others. Then, based on node degree and neighbors node degree; and nodes strength and neighbors node strength, the Weighted Strength Degree (WSD) and the Clustering Impact Coefficient (CIC) of a node is determined. Finally, the importance of a node based on its willingness to propagate information in the network is done by accounting the Influence (In) and CIC of the node. The temporal evolution graph and time-aggregated graph models are used to capture the topology dynamics of the mobile social networks. Also, Susceptible-Infected-Recovered (SIR) model is used to evaluate the performance of NWI to disseminate information in real-world networks. Results show the effectiveness of the proposed method to extract important nodes in MSNs.

Social identity–aware opportunistic routing in mobile social networks

AbstractMobile social networks, as a type of delay‐tolerant networks, are composed of nodes with different social properties in which information can be shared among them through opportunistic links. In such networks, it is important to analyze social properties of mobile nodes for transmitting information because they lack a stable source‐to‐destination delivery path and connect intermittently. Many social‐aware routing algorithms have been proposed to address these problems. However, these algorithms fail to achieve an optimal performance since they ignored an important social property, social identity. In this paper, we propose a social identity–aware opportunistic routing algorithm by considering social identities and social influences of mobile nodes in mobile social networks. It is shown that the social identity–aware opportunistic routing algorithm substantially improves the routing performance compared with the existing algorithms according to the simulation results in 2 real‐world human contact traces.

A service recovery method based on trust evaluation in mobile social network

Mobile social network makes users create and share multimedia contents freely and conveniently. However, some nodes in mobile social network have malicious behavior, such as discarding or tampering packet. These factors will cause service interruptions in the process of providing multimedia contents for the user. When the service interruption happens, how to choose the more reliable backup device, reduce interruption number, increase the packet transmission efficiency and improve user's experience of sharing multimedia contents is the object of this paper. We propose a service recovery method based on trust evaluation which adopts Dempster-Shafer (D-S) evidence theory. The service requester calculates the direct trust degree and the recommended trust degree of the backup devices, then uses the evidence combination rule to calculate the comprehensive trust degree. The backup device with the highest trust value will be seclected to recover the service. The simulation results show that this method effectively improves the packet delivery ratio, reduces the service execution time and provides users with more stable multimedia contents.

Social-aware data dissemination service in mobile social network with controlled overhead

The popularity of Mobile Social Networks (MSNs) paves a way for mobile users to download or view their interested contents not only from access points of Internet but also from other mobile users through short-range radio communications like Bluetooth and Wi-Fi. Through communications between users in MSNs, the bandwidth of short-range communications can be better utilized and relieve the stress on the bandwidth of cellular network. However, due to intermittent connectivity in MSNs, nodes in MSNs can only forward messages opportunistically. Therefore, how to disseminate data accurately and efficiently turns out as a challenging problem. Most previous works focus more on increasing delivery ratio while paying less attention on reducing overhead measures the number of copies that are stored and spread by relay nodes that are not interested at all in the contents. Note that high data dissemination ratio is usually accompanied by high and uncontrolled overhead, resulting in heavy storage burden on the nodes in MSNs and low quality of data dissemination. This motivates us to propose a new data dissemination scheme to maximize data dissemination ratio with controlled overhead. And more specifically, with given limitation on the overhead of spreading the messages, we design a new data dissemination scheme to maximize data dissemination ratio. In our data dissemination scheme, a time-homogeneous Markov model is designed to analyze the interest transitions of every node’s neighbors to decide when to forward the message and which node is chosen to forward the message further. Furthermore, two utility functions are investigated to evaluate the service ability of nodes for each kind of interest among the messages. The experimental results on simulated and real datasets demonstrate that our new scheme can outperform existing protocols with higher delivery ratio and lower delivery latency under controlled overheads.

The Dynamic Influence Graph Model on Mobile Datasets

With the rapid development of mobile technologies, more and more people are equipped with smartphones. It is possible for scientists to collect and analyze mobile data efficiently. Mobile data contain rich semantic as well as topological information. Rich information can be inferred from these data such as social influence among different nodes in mobile social network. However, it is difficult to estimate the strength of social influence due to the characteristics of inherent dynamic and large scale of mobile social network. In this paper, a Dynamic Influence Graph (DIG) model is proposed which utilizes temporal information in a topological perspective, and an efficient algorithm is proposed based on the DIG model. The proposed algorithm can calculate social influence between any two nodes in a given mobile social network stream segment, and takes edge weights, node connectivity and temporal information into consideration. Experimental results with a real mobile social network dataset show that the proposed approach can infer social influence and achieve a-state-of-the-art accuracy (82-86%) efficiently and automatically.

Measures of node centrality in mobile social networks

Mobile social networks exploit human mobility and consequent device-to-device contact to opportunistically create data paths over time. While links in mobile social networks are time-varied and strongly impacted by human mobility, discovering influential nodes is one of the important issues for efficient information propagation in mobile social networks. Although traditional centrality definitions give metrics to identify the nodes with central positions in static binary networks, they cannot effectively identify the influential nodes for information propagation in mobile social networks. In this paper, we address the problems of discovering the influential nodes in mobile social networks. We first use the temporal evolution graph model which can more accurately capture the topology dynamics of the mobile social network over time. Based on the model, we explore human social relations and mobility patterns to redefine three common centrality metrics: degree centrality, closeness centrality and betweenness centrality. We then employ empirical traces to evaluate the benefits of the proposed centrality metrics, and discuss the predictability of nodes' global centrality ranking by nodes' local centrality ranking. Results demonstrate the efficiency of the proposed centrality metrics.