  The problem of influence maximization is to select the most influential
individuals in a social network. With the popularity of social network sites,
and the development of viral marketing, the importance of the problem has been
increased. The influence maximization problem is NP-hard, and therefore, there
will not exist a polynomial-time algorithm to solve the problem unless P=NP.
Many heuristics are proposed to find a nearly good solution in a shorter time.
  In this paper, we propose two heuristic algorithms to find good solutions.
The heuristics are based on two ideas: (1) vertices of high degree have more
influence in the network, and (2) nearby vertices influence on almost analogous
sets of vertices. We evaluate our algorithms on several well-known data sets
and show that our heuristics achieve better results (up to $15\%$ in influence
spread) for this problem in a shorter time (up to $85\%$ improvement in the
running time).
