Identification of Influential Nodes via Effective Distance-based Centrality Mechanism in Complex Networks

Aman Ullah,Jinfang Sheng,Jun Long,Bin Wang,Nasrullah Khan,Lucia Valentina Gambuzza

doi:10.1155/2021/8403738

Abstract

Efficient identification of influential nodes is one of the essential aspects in the field of complex networks, which has excellent theoretical and practical significance in the real world. A valuable number of approaches have been developed and deployed in these areas where just a few have used centrality measures along with their concerning deficiencies and limitations in their studies. Therefore, to resolve these challenging issues, we propose a novel effective distance-based centrality (EDBC) algorithm for the identification of influential nodes in concerning networks. EDBC algorithm comprises factors such as the power of K-shell, degree nodes, effective distance, and numerous levels of neighbor’s influence or neighborhood potential. The performance of the proposed algorithm is evaluated on nine real-world networks, where a susceptible infected recovered (SIR) epidemic model is employed to examine the spreading dynamics of each node. Simulation results demonstrate that the proposed algorithm outperforms the existing techniques such as eigenvector, betweenness, closeness centralities, hyperlink-induced topic search, H-index, K-shell, page rank, profit leader, and gravity over a valuable margin.

Highlights

In recent years, complex networks are an attractive and hot research area by virtue of its wide range of practical and theoretical applications in many major fields [1,2,3,4,5]
It calculates the degree and K-shell, so the time complexity becomes O(|M|). It computes the effective distance among the nodes and their adjacent or neighbors. e distance between the nearest and next-nearest neighbors in the entire network is calculated. erefore, the computational time complexity is O (N < K > 2), and in the third and fourth phases, the node’s influence of all neighbors will be calculated. erefore, effective distance-based centrality (EDBC) algorithm’s total time complexity can become O(N < k > 2 + |M|), where N and k denote the number of nodes and average degree of nodes in the network, respectively
It is suitable for directed networks but not for undirected and unweighted, but EDBC is appropriate in case of any type of network whether it is directed or undirected. hyperlink-induced topic search (HITS) [25] is a link analysis technique that uses various metrics concurrently

Summary

Introduction

Complex networks are an attractive and hot research area by virtue of its wide range of practical and theoretical applications in many major fields [1,2,3,4,5]. We propose a new, efficient, and effective method termed as EDBC algorithm for key or influential nodes identification in complex networks. (1) A new ranking centrality perspective: from the last two decades, several models and algorithms regarding the identification of influential or key nodes have been developed but still it is a challenge In this regard, we propose a novel effective distance-based centrality algorithm which is comprising of several features to have experimented on unweighted networks and structure to sort out the important nodes. Inspired from [44, 53], we proposed an efficient algorithm called EDBC, which effectively identifies the highly influential nodes in various scales of complex networks

EDBC Model

Preliminaries

Experimentation and Results’ Analysis

Evaluation Metrics

Conclusion and Future