  The influence maximization problem is trying to identify a set of K nodes by
which the spread of influence, diseases, or information is maximized. The
optimization of influence by finding such a set is an NP-hard problem and a key
issue in analyzing complex networks. In this paper, a new greedy and hybrid
approach based on a community detection algorithm and a MADM technique (TOPSIS)
is proposed to cope with the problem, called, Greedy TOPSIS and Community-Based
(GTaCB) algorithm. The paper concisely introduces community detection and the
TOPSIS technique, then it presents the pseudo-code of the proposed algorithm.
Afterward, it compares the performance of the solution which is found by GTaCB
with some well-known greedy algorithms, based on Degree Centrality, Closeness
Centrality, Betweenness Centrality, PageRank as well as TOPSIS, from two
aspects: diffusion quality and diffusion speed. In order to evaluate the
performance of GTaCB, computational experiments on nine different types of
real-world networks are provided. The tests are conducted via one of the
renowned epidemic diffusion models, namely, Susceptible-Infected-Recovered
(SIR) model. The simulations exhibit that in most of the cases the proposed
algorithm significantly outperforms the others, chiefly as the number of
initial nodes or probability of infection increases.

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