Efficient analysis of node influence based on SIR model over huge complex networks

2014 ◽  
Author(s):  
Masahiro Kimura ◽  
Kazumi Saito ◽  
Kouzou Ohara ◽  
Hiroshi Motoda
Keyword(s):  
Complex Networks ◽  
Sir Model

scholarly journals Identifying Influential Nodes in Complex Networks Based on Node Itself and Neighbor Layer Information

Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1570 ◽  
Author(s):  
Jingcheng Zhu ◽  
Lunwen Wang
Keyword(s):  
Complex Networks ◽  
Sir Model ◽  
Identification Accuracy ◽  
Degree Centrality ◽  
Network Stability ◽  
Propagation Process ◽  
Calculation Results ◽  
Influential Nodes ◽  
Nearest Neighborhood ◽  
Accuracy Degree

Identifying influential nodes in complex networks is of great significance for clearly understanding network structure and maintaining network stability. Researchers have proposed many classical methods to evaluate the propagation impact of nodes, but there is still some room for improvement in the identification accuracy. Degree centrality is widely used because of its simplicity and convenience, but it has certain limitations. We divide the nodes into neighbor layers according to the distance between the surrounding nodes and the measured node. Considering that the node’s neighbor layer information directly affects the identification result, we propose a new node influence identification method by combining degree centrality information about itself and neighbor layer nodes. This method first superimposes the degree centrality of the node itself with neighbor layer nodes to quantify the effect of neighbor nodes, and then takes the nearest neighborhood several times to characterize node influence. In order to evaluate the efficiency of the proposed method, the susceptible–infected–recovered (SIR) model was used to simulate the propagation process of nodes on multiple real networks. These networks are unweighted and undirected networks, and the adjacency matrix of these networks is symmetric. Comparing the calculation results of each method with the results obtained by SIR model, the experimental results show that the proposed method is more effective in determining the node influence than seven other identification methods.

Identifying and ranking influential spreaders in complex networks by combining a local-degree sum and the clustering coefficient

2018 ◽  
Vol 32 (06) ◽  
pp. 1850118 ◽  
Author(s):  
Mengtian Li ◽  
Ruisheng Zhang ◽  
Rongjing Hu ◽  
Fan Yang ◽  
Yabing Yao ◽  
...  
Keyword(s):  
Complex Networks ◽  
Nearest Neighbors ◽  
Sir Model ◽  
Clustering Coefficient ◽  
Degree Centrality ◽  
Competitive Performance ◽  
Spreading Process ◽  
Degree Sum ◽  
Local Degree ◽  
Crucial Problem

Identifying influential spreaders is a crucial problem that can help authorities to control the spreading process in complex networks. Based on the classical degree centrality (DC), several improved measures have been presented. However, these measures cannot rank spreaders accurately. In this paper, we first calculate the sum of the degrees of the nearest neighbors of a given node, and based on the calculated sum, a novel centrality named clustered local-degree (CLD) is proposed, which combines the sum and the clustering coefficients of nodes to rank spreaders. By assuming that the spreading process in networks follows the susceptible–infectious–recovered (SIR) model, we perform extensive simulations on a series of real networks to compare the performances between the CLD centrality and other six measures. The results show that the CLD centrality has a competitive performance in distinguishing the spreading ability of nodes, and exposes the best performance to identify influential spreaders accurately.

scholarly journals Identifying Multiple Influential Spreaders in Complex Networks by Considering the Dispersion of Nodes

2022 ◽  
Vol 9 ◽  
Author(s):  
Li Tao ◽  
Mutong Liu ◽  
Zili Zhang ◽  
Liang Luo
Keyword(s):  
Complex Networks ◽  
Independent Set ◽  
Sir Model ◽  
Second Order ◽  
Propagation Process ◽  
Ranking Problem ◽  
Local Index ◽  
Practical Applications ◽  
Node Ranking ◽  
New Algorithms

Identifying multiple influential spreaders, which relates to finding k (k > 1) nodes with the most significant influence, is of great importance both in theoretical and practical applications. It is usually formulated as a node-ranking problem and addressed by sorting spreaders’ influence as measured based on the topological structure of interactions or propagation process of spreaders. However, ranking-based algorithms may not guarantee that the selected spreaders have the maximum influence, as these nodes may be adjacent, and thus play redundant roles in the propagation process. We propose three new algorithms to select multiple spreaders by taking into account the dispersion of nodes in the following ways: (1) improving a well-performed local index rank (LIR) algorithm by extending its key concept of the local index (an index measures how many of a node’s neighbors have a higher degree) from first-to second-order neighbors; (2) combining the LIR and independent set (IS) methods, which is a generalization of the coloring problem for complex networks and can ensure the selected nodes are non-adjacent if they have the same color; (3) combining the improved second-order LIR method and IS method so as to make the selected spreaders more disperse. We evaluate the proposed methods against six baseline methods on 10 synthetic networks and five real networks based on the classic susceptible-infected-recovered (SIR) model. The experimental results show that our proposed methods can identify nodes that are more influential. This suggests that taking into account the distances between nodes may aid in the identification of multiple influential spreaders.

An SIR model with infection delay and propagation vector in complex networks

2012 ◽  
Vol 69 (3) ◽  
pp. 927-934 ◽  
Author(s):  
Chengyi Xia ◽  
Li Wang ◽  
Shiwen Sun ◽  
Juan Wang
Keyword(s):  
Complex Networks ◽  
Sir Model ◽  
Propagation Vector

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

2017 ◽  
Vol 28 (01) ◽  
pp. 1750014 ◽  
Author(s):  
Fan Yang ◽  
Ruisheng Zhang ◽  
Zhao Yang ◽  
Rongjing Hu ◽  
Mengtian Li ◽  
...  
Keyword(s):  
Complex Networks ◽  
Nearest Neighbors ◽  
Sir Model ◽  
Spreading Process ◽  
Developing Strategies

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.

scholarly journals Social Stability Risk Diffusion of Large Complex Engineering Projects Based on an Improved SIR Model: A Simulation Research on Complex Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Zhengqi He ◽  
Dechun Huang ◽  
Junmin Fang
Keyword(s):  
Complex Networks ◽  
Interpersonal Relationship ◽  
Sir Model ◽  
Social Stability ◽  
Simulation Research ◽  
Social Risks ◽  
Large Complex ◽  
The Social ◽  
Complex Engineering ◽  
Engineering Projects

The development of China’s new urbanization has driven the rapid increase in large complex engineering projects, which have the characteristics of large-scale investment, long-term construction, and wide social influence, easily causing benefit conflicts among relevant stakeholders, and breaking out social stability risks. In the previous research, the risks of large complex engineering projects mainly concentrated on the assessment of economic risk, schedule risk, etc. However, there were few studies on social risks, and they did not consider how the risks spread on the complex networks based on the social connections such as interpersonal relationship. From the subject of social stability risk diffusion of large complex engineering projects, this paper constructs a related risk diffusion model based on the SIR model to analyze risk diffusion mechanism. Through NetLogo simulation platform, the model is placed under a small-world network environment that is closest to the topology structure of real social interpersonal relationship network for simulation research, aiming to find out key factors of social stability risk intervention for large complex engineering projects, which greatly contributes to the social stability risk management of large complex engineering projects.

scholarly journals Locating Multiple Sources of Contagion in Complex Networks under the SIR Model

2019 ◽  
Vol 9 (20) ◽  
pp. 4472 ◽  
Author(s):  
Xiang Li ◽  
Yangyang Liu ◽  
Chengli Zhao ◽  
Xue Zhang ◽  
Dongyun Yi
Keyword(s):  
Complex Networks ◽  
Incomplete Information ◽  
Human Life ◽  
Sir Model ◽  
Multiple Sources ◽  
Propagation Process ◽  
Localization Accuracy ◽  
Localization Method ◽  
Label Method ◽  
New Perspective

Simultaneous outbreaks of contagion are a great threat against human life, resulting in great panic in society. It is urgent for us to find an efficient multiple sources localization method with the aim of studying its pathogenic mechanism and minimizing its harm. However, our ability to locate multiple sources is strictly limited by incomplete information about nodes and the inescapable randomness of the propagation process. In this paper, we present a valid approach, namely the Potential Concentration Label method, which helps locate multiple sources of contagion faster and more accurately in complex networks under the SIR(Susceptible-Infected-Recovered) model. Through label assignment in each node, our aim is to find the nodes with maximal value after several iterations. The experiments demonstrate that the accuracy of our multiple sources localization method is high enough. With the number of sources increasing, the accuracy of our method declines gradually. However, the accuracy remains at a slight fluctuation when average degree and network scale make a change. Moreover, our method still keeps a high multiple sources localization accuracy with noise of various intensities, which shows its strong anti-noise ability. I believe that our method provides a new perspective for accurate and fast multi-sources localization in complex networks.

scholarly journals Stability Analysis of SIR Model with Distributed Delay on Complex Networks

PLoS ONE ◽  
2016 ◽  
Vol 11 (8) ◽  
pp. e0158813 ◽  
Author(s):  
Chuangxia Huang ◽  
Jie Cao ◽  
Fenghua Wen ◽  
Xiaoguang Yang
Keyword(s):  
Stability Analysis ◽  
Complex Networks ◽  
Distributed Delay ◽  
Sir Model
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