  In this paper, we propose a new averaging model for modeling the competitive
influence of $K$ candidates among $n$ voters in an election process. For such
an influence propagation model, we address the question of how many seeded
voters a candidate needs to place among undecided voters in order to win an
election. We show that for a random network generated from the stochastic block
model, there exists a percolation threshold for a candidate to win the election
if the number of seeded voters placed by the candidate exceeds the threshold.
By conducting extensive experiments, we show that our theoretical percolation
thresholds are very close to those obtained from simulations for random
networks and the errors are within $10\%$ for a real-world network.
