Abstract
The problem of influence maximization has grown in popularity and interest in recent decades due to its valuable application in various fields. This problem mainly focuses on identifying Top-K influential users that, when selected, the influence spread will be maximized. Thus, the identification of such nodes is pivotal in increasing the adoption of promoted information and behavior within the network. In this paper, we propose two new efficient algorithms, namely, "MinCDegKatz d-hops (MaxCDegKatz d-hops)" that relies on a combination of centralization measures due to their known efficiency and performance in terms of influence spread and their low runtime complexity. The proposed algorithms combine between the degree centrality as local measure and the Katz centrality as global centrality metric on a graph with preselected weight edges that should exceed a predefined threshold value. Thus, each selected seed set is separated by a number of hops that differ depending on the graph radius. Then, the influence spread is measured for the two proposed algorithms under the Independent Cascade (IC) and Linear Threshold (LT) models. We conducted extensive experiments on a large-scale graph that demonstrated the performance of our proposed algorithms against existing approaches in term of spreading ability and time complexity. (C) 2020 Elsevier Inc. All rights reserved.