Modeling the spread of infectious diseases through influence maximization

Yao, Shunyu; Fan, Neng; Hu, Jie

doi:10.1007/s11590-022-01853-1

Cited by 17 publications

(3 citation statements)

References 40 publications

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“…In each system, individuals and their relationships can be represented as networks consisting of nodes and edges 1 , 2 . Recently, the identification of significant nodes in complex networks has gained significant attention from researchers, providing a new perspective for understanding the objective world and facilitating a better comprehension of the spread of diseases 3 – 5 , power grid protection 6 , information dissemination 7 – 9 , protein discovery 10 , and immunization strategies 11 , 12 , among other fields 13 – 15 .…”

Section: Introductionmentioning

confidence: 99%

Identifying influential nodes in complex networks using a gravity model based on the H-index method

Zhu,

Zhan,

2023

Sci Rep

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Identifying influential spreaders in complex networks is a widely discussed topic in the field of network science. Numerous methods have been proposed to rank key nodes in the network, and while gravity-based models often perform well, most existing gravity-based methods either rely on node degree, k-shell values, or a combination of both to differentiate node importance without considering the overall impact of neighboring nodes. Relying solely on a node's individual characteristics to identify influential spreaders has proven to be insufficient. To address this issue, we propose a new gravity centrality method called HVGC, based on the H-index. Our approach considers the impact of neighboring nodes, path information between nodes, and the positional information of nodes within the network. Additionally, it is better able to identify nodes with smaller k-shell values that act as bridges between different parts of the network, making it a more reasonable measure compared to previous gravity centrality methods. We conducted several experiments on 10 real networks and observed that our method outperformed previously proposed methods in evaluating the importance of nodes in complex networks.

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Section: Introductionmentioning

confidence: 99%

Identifying influential nodes in complex networks using a gravity model based on the H-index method

Zhu,

Zhan,

2023

Sci Rep

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“…Rapidly pinpointing such 'super-spreaders' could potentially truncate the transmission pathways of the disease. Presently, various facets of life involve the issue of identifying key nodes, such as disease prevention and control [8], safeguarding vulnerable nodes in trade networks [9], and curtailing the propagation of rumors in online social networks [10]. Consequently, the efficient and accurate localization of key nodes within networks constitutes a matter of significant practical importance.…”

Section: Introductionmentioning

confidence: 99%

Identifying key spreaders in complex networks based on local clustering coefficient and structural hole information

Wang,

Liu

et al. 2023

New J. Phys.

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Identifying key spreaders in a network is one of the fundamental problems in the field of complex network research, and accurately identifying influential propagators in a network holds significant practical implications. In recent years, numerous effective methods have been proposed and widely applied. However, many of these methods still have certain limitations. For instance, some methods rely solely on the global position information of nodes to assess their propagation influence, disregarding local node information. Additionally, certain methods do not consider clustering coefficients, which are essential attributes of nodes. Inspired by the quality formula, this paper introduces a method called Structural Neighborhood Centrality (SNC) that takes into account the neighborhood information of nodes. SNC measures the propagation power of nodes based on first and second-order neighborhood degrees, local clustering coefficients, structural hole constraints, and other information, resulting in higher accuracy. A series of pertinent experiments conducted on 12 real-world datasets demonstrate that, in terms of accuracy, SNC outperforms methods like CycleRatio and KSGC. Additionally, SNC demonstrates heightened monotonicity, enabling it to distinguish subtle differences between nodes. Furthermore, when it comes to identifying the most influential Top-k nodes, SNC also displays superior capabilities compared to the aforementioned methods. Finally, we conduct a detailed analysis of SNC and discuss its advantages and limitations.

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“…[2] The critical node detection problem (CNDP) is typical in complex network analysis, where its enhancement or failure will significantly improve or degrade the functionality of the target network and can better understand or control the behavior of nodes in complex networks. [3] Identifying a set of influential and important nodes in complex networks plays a crucial role in advertising placement, [4] rumor control, [5] epidemic prevention and control, [6] etc. With the complexity of human society and the increasing demand for large-scale, complex systems, the study of the importance of complex network nodes has received increasing attention.…”

Section: Introductionmentioning

confidence: 99%

SLGC: Identifying influential nodes in complex networks from the perspectives of self-centrality, local centrality, and global centrality

Ai 艾,

Liu 刘,

Kang 康

et al. 2023

Chinese Phys. B

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Identifying influential nodes in complex networks and ranking their importance plays an important role in many fields such as public opinion analysis, marketing, epidemic prevention and control. To solve the issue of the existing node centrality measure only considers the specific statistical feature of a single dimension, a SLGC model is proposed that combines a node's self-influence, its local neighborhood influence, and global influence to identify influential nodes in the network. The exponential function of e is introduced to measure the node's self-influence; in the local neighborhood, the node's one-hop neighboring nodes and two-hop neighboring nodes are considered, while the information entropy is introduced to measure the node's local influence; the topological position of the node in the network and the shortest path between nodes are considered to measure the node's global influence. To demonstrate the effectiveness of the proposed model, extensive comparison experiments are conducted with eight existing node centrality measures on six real network data sets using node differentiation ability experiments, susceptible-infected-recovered (SIR) model and network efficiency as evaluation criteria. The experimental results show that the method can identify influential nodes in complex networks more accurately.

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Modeling the spread of infectious diseases through influence maximization

Cited by 17 publications

References 40 publications

Identifying influential nodes in complex networks using a gravity model based on the H-index method

Identifying influential nodes in complex networks using a gravity model based on the H-index method

Identifying key spreaders in complex networks based on local clustering coefficient and structural hole information

SLGC: Identifying influential nodes in complex networks from the perspectives of self-centrality, local centrality, and global centrality

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