Information Diffusion Within a Limited Budget Using Node Centralities and Community Detection

Optimal seed node selection method for LTIS model

“…Although the implementation of this method is done on small datasets due to its high time/space complexity, this method is useful for providing an optimal solution for seed node selection. In this article, seed sets are selected by using seven different seed sets selection schemes, namely, degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, K‐core decomposition method, entropy method and proposed method in the paper by Devi and Tripathi 17 which is denoted as DEC in our simulation results. We have compared our proposed advantage‐of‐seed‐node method with the above mentioned seven existing methods in terms of influence diffusion under the LTIS model.…”

“…16 This article proposed a hybrid degree centrality method to evaluate the node's influence. Devi and Tripathi 17 proposed a method that uses the value of degree and closeness centralities of the nodes within a community in the network to calculate the influence diffusion. Akar and Dalgic 18 have undergone a detailed study to understand the online consumer's purchasing behavior by investigating different centrality scores of the users in a social network.…”

“…The method in the paper by Devi and Tripathi 17 is named as the DEC method in our simulation performance. Influence spread of the seed node is calculated by clustering the network using into smaller clusters using “Girvan Newman algorithm on the edge‐betweenness centrality” 39 and finding the value of $$$ {P}_r(x) $$$ of the each node where, $$$$ {P}_r(x)={U}_D(x)\times {U}_C(x).$$…”