Identifying Influential Nodes for Smart Enterprises Using Community Structure With Integrated Feature Ranking

Abstract

Finding influential nodes reshuffles the very notion of linear paths in business processes and replaces it with networks of business value within a smart enterprise system. There are many existing algorithms for identifying influential nodes with certain limitations for applying in large-scale networks. In this paper, we propose a community structure with an Integrated Features Ranking (CIFR) algorithm to find influential nodes in the network. Firstly, we use the community detection algorithm to find communities in the system and then we rank the nodes of network based on three factors, namely-local ranking, gateway ranking, and community ranking, collectively termed as integrated features. Our algorithm intends to select influential nodes, which are both globally and locally optimal, leading to overall high information propagation. We perform the experimental results on total eight networks using various evaluation parameters. The obtained results validate superior performance against contemporary algorithms adding value to smart enterprises.

HIKS: A K‐shell‐weighted hybrid approach method for detecting influential nodes in complex networks using possible edge weights

“…14 The explanation for this is that these measurements ignore edge weights, which might contain important information about the significance of nodes and the potency of their connections. 15,16 Additionally, conventional centrality measurements frequently presume that all edges are equally essential, which may be false. Various methods have been proposed to address these limitations, incorporating edge weights in centrality measures.…”

“…Still, recent research has indicated that they might not be adequate for correctly identifying prominent nodes 14 . The explanation for this is that these measurements ignore edge weights, which might contain important information about the significance of nodes and the potency of their connections 15,16 . Additionally, conventional centrality measurements frequently presume that all edges are equally essential, which may be false.…”

Identifying influential spreaders in complex networks based on density entropy and community structure

“…On the other hand, the influence of community structure on node centrality in complex networks has gained extensive attention from researchers. Kumar et al [23] proposed a community structure algorithm called CIFR which integrates feature ordering to detect influential nodes using the characteristics of community propagation in the network. Sun et al [24] introduced a new community-based K-shell decomposition algorithm called CKS.…”

QST-ClustNet: Quantum Inspired Sooty Tern Optimization for Clustering User Profiles in Social Network

“…An essential technique for social network analysis is community discovery, which will be created to find groups of nodes that share characteristics. In particular, community detection is a crucial component of network analysis that has been used in numerous practical applications, such as fraud detection, community detection, recommendation systems, influential node detection, and link prediction, among others [1][2][3][4]. Common community detection methods look for strong intracommunity and low inter-community similarity, which indicates that nodes inside a community have high pairwise similarity scores while nodes in nearby communities have low pairwise similarity scores, to locate node clusters [5].…”