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 large-scale 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 TermsMobile social network, influence maximization, PCA 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 TermsMobile social network, Influence maximization, community greedy algorithm, Location based community greedy algotithm.

The study was conducted in Bhatkuli Panchayat Samiti of Amravati district of Maharashtra state. The study conducted on impact of MGNREGS on beneficiaries. It was revealed that majority of respondents were middle aged group, educated high school, schedule caste class, marginal farmer, farming + labourer main occupation, half of the beneficiaries under BPL level, nuclear type of family, medium family size, medium level of source of information, low social participation and more than half of beneficiaries had medium impact. Age, education, caste, size of land holding, occupation, annual income, type of family and size of family had positive significant relationship with impact of MGNREGS. Source of information negatively non-significant and social participation had non-significant relationship with impact of MGNREGS.