Introduction of Influence maximization in mobile social network

                The mobile social network has a lot of influence on the communities. The information on social media spread very fast and in has a lot of influence in the lives on individuals in community. In this report, there is a discussion on finding the most influential nodes. The community based greedy algorithms is used to forming top-k influential nods that have maximizing impact on communities. Mobile social network systems, with the wireless technologies and the proliferation of mobile devices, are increasingly available. An essential role is being played by the mobile social network in influence in the “word of mouth” form and spread of information. A technique of divide-and-conquer with mechanism of parallel computing has been used (SONG, Guojie et al., 2015).

Algorithms of Influence maximization in mobile social network

Community based greedy algorithms of Influence maximization in mobile social network

            It has been shown that good approximation can be given by a Greedy algorithm having provable approximation guarantees; however, if it is not prohibitive then it is computationally expensive to implement a greedy algorithm on the large mobile social network (Bhosale & Kulkarni, 2015). First, an algorithm is proposed called Community-based Greedy algorithm so that top-K influential nodes could be mined. Precisely, influence maximization is itself a problem of a small subset of nodes' finding in a social network that is able to maximize a spread of influence. Unfortunately, the problem is NP-hard of finding the most influential nodes (Song, Zhou, Wang, & Xie, 2015).

Community Greedy Algorithm Based on Location of Influence maximization in mobile social network

Goals of Influence maximization in mobile social network

            The main goal of the social media network influence is to parallelize propagation based on communities and consider the influence propagation across the communities.  The goals of using the design approach and greedy algorithms are to use both the global structure that contain the network information and local attributes for the analysis of social influence (SONG, Guojie et al., 2015).

Encompassed of Influence maximization in mobile social network

Further improvement performances of Influence maximization in mobile social network

            Two components are encompassed; selecting communities so that influential nodes could be found by a dynamic programming and taking into account information to divide large mobile social networks into various communities. For further performance improvement, influence propagation is parallelized based on communities. Moreover, a precision analysis is given in this study to show the approximation guaranteed of proposed models. The Performance can be augmented by increasing the propagation of influence hold up communities and take into account crossing communities influence propagation.

Dividing the large scale mobile network of Influence maximization in mobile social network

            For large scale mobile network division finding a subset of influential individuals is a fundamental issue in the mobile social network such that initially targeting them will contribute towards maximizing the spread of influence. Influence maximization in mobile social network aims to look for the small individuals’ group having maximal influence cascades. The large-scale mobile social networks experiments demonstrate that planned algorithm is faster as compare with any past algorithms (NGUYEN, Dung T. et al., 2013).

Influential nodes by dynamic program of Influence maximization in mobile social network

            The formula of dynamic programming is used for selecting communities to locate nodes that are authoritative. The LCGA algorithm believes both significant factor of time & location. The mobile social network experiential studies demonstrate that LCGA algorithms have development on both effectiveness and correctness compared with methods of CGA.

Divide and conquer of Influence maximization in mobile social network

            This study has adopted the divide and conquers strategy with mechanisms that are parallel computing. For mining top-K influential nodes greedy rule based on community is used primary. It has two divisions: large- scale mobile social network division into a lot of communities by taking data diffusion under consideration. The Communities are also likely to select significant dynamic programming nodes (SONG, Guojie et al., 2015).

Parallel computing mechanism of Influence maximization in mobile social network

                The Parallel computing mechanism is also used in the algorithm for maximizing the influence of mobile social network.  A model of parallel programming is a concept of parallel computer structural design, by using this it is suitable to articulate algorithms and the composition in the whole program. The programming model value can judge on its generalization: to know how well different problems range can be uttered for an assortment of diverse architectures: and how proficiently the execution of compiled programs. The parallel programming model implementation can get the library form appeal to a sequential language in the algorithm, as an addition to an accessible language, or as new language (BHOSALE, Smita and Kulkarni, Dhanshree, 2015).

Conclusion on Influence maximization in mobile social network

            Summing up the discussion it can be said that the Mobile social network systems, with the wireless technologies and the proliferation of mobile devices, are increasingly available.  Influence maximization is itself a problem of a small subset of nodes' finding in a social network that is able to maximize a spread of influence. The precision analysis is given in this study to show the approximation guaranteed of proposed models. Influence maximization in mobile social network aims to look for the small individuals’ group having maximal influence. The Communities are also likely to select significant dynamic programming nodes

References of Influence maximization in mobile social network

BHOSALE, Smita and Dhanshree KULKARNI. 2015. Influence Maximization on Mobile Social Network using Location based Community Greedy Algorithm. International Journal of Computer Applications (0975 – 8887), Volume 122, Issue: 19., pp.28-31.

NGUYEN, Dung T., Soham DAS, and My T. THAI. 2013. Influence Maximization in Multiple Online Social Networks. IEEE., pp.3060-3065.

SONG, Guojie, Xiabing ZHOU, Yu WANG, and Kunqing XIE. 2015. Influence Maximization on Large-Scale Mobile Social Network: A Divide-and-Conquer Method. IEEE Transactions on Parallel and Distributed Systems, Volume: 26, Issue: 5., pp.1379 - 1392.