Influence Node Analysis using Monte Carlo and Clonal Selection Algorithm for Influence Maximization
K. Geetha, Aysha Naseer, M. Dhanalakshmi · Zenodo (CERN European Organization for Nuclear Research) · 2021
In viral marketing, Influence maximization (IM) is the most prominent and significant process of determining the subsets of nodes in social network that enhance the spread of information. Many of the approaches proposed for the influence maximization incur low efficiency due to massive data and are found to be inadequate to cover large amount of prevailing data. In this research, Influence Maximization based on Clonal Selection Algorithm (IM-CSA) is proposed to increase the performance of the Influence maximization process. The proposed method involves in detecting the community based on Louvain method and Monte Carlo simulation technique is applied for Independent Cascade. In this proposed approach, to determine the probability of influence value of nodes, Monte Carlo method is applied to perform the sensitive analysis on the input parameters. The Clonal Selection Algorithm (CSA) has the adaptive unit with competitive process of selection that improves the adaptive fit in the information. In this CSA method, the nodes in the network are sorted based on their influence values and in turn the nodes with less influence are removed which helps to significantly reduce the computational time and memory usage of the proposed method. The most influence nodes in the network are determined by the mutation in the CSA method. The proposed method is evaluated using real world dynamic network datasets and it is empirically found that the proposed IM-CSA method shows higher performance than the existing methods in terms of time and influence spread.