Identifying the most influential spreaders in complex networks by an Extended Local K-Shell Sum

Abstract

Identifying influential spreaders is crucial for developing strategies to control the spreading process on complex networks. Following the well-known K-Shell (KS) decomposition, several improved measures are proposed. However, these measures cannot identify the most influential spreaders accurately. In this paper, we define a Local K-Shell Sum (LKSS) by calculating the sum of the K-Shell indices of the neighbors within 2-hops of a given node. Based on the LKSS, we propose an Extended Local K-Shell Sum (ELKSS) centrality to rank spreaders. The ELKSS is defined as the sum of the LKSS of the nearest neighbors of a given node. By assuming that the spreading process on networks follows the Susceptible-Infectious-Recovered (SIR) model, we perform extensive simulations on a series of real networks to compare the performance between the ELKSS centrality and other six measures. The results show that the ELKSS centrality has a better performance than the six measures to distinguish the spreading ability of nodes and to identify the most influential spreaders accurately.

A Key Node Mining Method Based on Acupoint‐Disease Network (ADN): A New Perspective for Exploring Acupoint Specificity

“…Resolution is a common index to measure the performance of algorithms, which can accurately distinguish the mining precision of each algorithm for nodes with highly similar centrality in large-scale networks [ 36 ]. In order to accurately compare the resolution index of each algorithm, we scored all 135 nodes involved in the data source and compared the granularity of all scoring results.…”

Weighted h-index for Identifying Influential Spreaders

“…The method in Ref. [18] was extended to identify the difference in spreading ability among nodes in the same shell [19][20][21][22][23][24][25][26][27][28][29][30][31]. For example, Zeng et al proposed a mixed degree decomposition (MDD) method by considering both the residual degree and the exhausted degree [20], but the optimal parameter λ is uncertain.…”

“…Xu et al designed an iterative neighbor information gathering (ING) process to rank the node influence [24]. Other measures [25][26][27][28][29][30][31][32][33], such as information index [32] and subgraph centrality [33], also have good performance in finding important nodes. These above-mentioned measures are structural centralities which measure the importance of a node based mainly on the topological structure of a network [10].…”

Ranking the spreading influence of nodes in complex networks based on mixing degree centrality and local structure

“…On the basis of these theories, some extended measures are proposed for determination. In order to express node importance more precisely, researchers put more emphasis on the topological structures of researching nodes and their neighbors, including extended coreness centrality [12], gravity centrality [13] and improved gravity centrality [14]. Meanwhile, in order to make the experimental results more practical, using the spreading model is also a very common way to determine the spreading capacity of nodes [15][16][17][18].…”