Identification of key recovering node for spatial networks

Zhang, Yan; Xia, Yongxiang; Guo, Lanwei; Zhu, Lingzhe; Liang, Yuanyuan; Tu, Haicheng

doi:10.1088/1674-1056/acb75f

Cited by 3 publications

(3 citation statements)

References 40 publications

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“…For each community, the candidate anti-rumor seeds has faster and broader influence propagation than other general nodes. [60][61][62] The higher the propagation capability, the larger probability that a node will be selected as a candidate antirumor seed. Inspired by three degree of separation [63,64] and community structure in networks, we define the potential local centrality (PLC) for calculating the propagation capability of nodes in each community.…”

Section: Selecting Candidate Anti-rumor Seedsmentioning

confidence: 99%

CRB: A new rumor blocking algorithm in online social networks based on competitive spreading model and influence maximization

Dong 董,

Xu 徐,

Meng 孟

2024

Chinese Phys. B

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The virtuality and openness of online social platforms make it a hotbed for the rapid propagation of various rumors. In order to block the outbreak of rumor, one of the most effective containment measures is spreading positive information to counterbalance the diffusion of rumor. The spreading mechanism of rumors and effective suppression strategies are significant and challenging research issues. Firstly, in order to simulate the dissemination of multiple types of information, we propose a competitive linear threshold model with state transition (CLTST) to describe the spreading process of rumor and anti-rumor in the same network. Subsequently, we put forward a community-based rumor blocking algorithm (CRB) based on influence maximization theory in social networks. Its crucial step is to identify a set of influential seeds that propagate anti-rumor information to other nodes, which includes community detection, selection of candidate anti-rumor seeds and generation of anti-rumor seed set. Under CLTST model, CRB algorithm has been compared with six state-of-the-art algorithms on nine online social networks to verify the performance. Experimental results show that the proposed model can better reflect the process of rumor propagation, and review the propagation mechanism of rumor and anti-rumor in online social networks. Moreover, the proposed CRB algorithm has better performance in weakening the rumor dissemination ability, which can select anti-rumor seeds in networks more accurately and achieve better performance in influence spread, sensitivity analysis, seeds distribution and running time.

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Section: Selecting Candidate Anti-rumor Seedsmentioning

confidence: 99%

CRB: A new rumor blocking algorithm in online social networks based on competitive spreading model and influence maximization

Dong 董,

Xu 徐,

Meng 孟

2024

Chinese Phys. B

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“…Examples include the rapid spread of false information on social media, [1,2] virus intrusion into computer networks, [3][4][5] the swift spread of negative or potentially harmful speech affecting national security, [6] as well as the emission of industrial pollutants and hazardous substances. [7,8] While much research has focused on studying information diffusion from the perspective of maximizing influence, [9,10] this paper addresses a reverse problem, namely source localization, which aims to accurately and efficiently identify the initial spreader of information.…”

Section: Introductionmentioning

confidence: 99%

Source localization in signed networks with effective distance

Ma 马,

Sun 孙,

Ding 丁

et al. 2024

Chinese Phys. B

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While progress has been made in information source localization, it has overlooked the prevalent friend and adversarial relationships in social networks. This paper addresses this gap by focusing on source localization in signed network models. Leveraging the topological characteristics of signed networks and transforming the propagation probability into effective distance, we propose an optimization method for observer selection. Additionally, by using the reverse propagation algorithm we present a method for information source localization in signed networks. Extensive experimental results demonstrate that a higher proportion of positive edges within signed networks contributes to more favorable source localization, and the higher the ratio of propagation rates between positive and negative edges, the more accurate the source localization becomes. Interestingly, this aligns with our observation that, in reality, the number of friends tends to be greater than the number of adversaries, and the likelihood of information propagation among friends is often higher than among adversaries. In addition, the source located at the periphery of the network is not easy to identify. Furthermore, our proposed observer selection method based on effective distance achieves higher operational effciency and exhibits higher accuracy in information source localization, compared with three strategies for observer selection based on the classical Full-order neighbor coverage.

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“…The stability of the network topology determines the performance of the entire network system, while the key nodes affect stability of the network [3][4][5] . Therefore, designing effective algorithms to identify key nodes in the network topology can significantly improve the stability and robustness of a network [6][7][8] .…”

Section: Introductionmentioning

confidence: 99%

Key node identification for a network topology using hierarchical comprehensive importance coefficients

Qiu,

Yu,

Feng

et al. 2024

Preprint

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Key nodes are similar to important hubs in a network structure, which can directly determine the robustness and stability of the network. By effectively identifying and protecting these critical nodes, the robustness of the network can be improved, making it more resistant to external interference and attacks. There are various topology analysis methods for existing networks, but key node identification methods often only focus on local attributes or global attributes. Designing an algorithm that combines both attributes can improve the accuracy of key node identification. In this paper, the constraint coefficient of a weakly connected network is calculated based on the Salton indicator, and the hierarchical tenacity global coefficient is obtained by an improved K-Shell decomposition method. Then, a hierarchical comprehensive node importance identification algorithm is proposed which can comprehensively indicate the local and global attributes of the network nodes. Experimental results on real network datasets show that the proposed algorithm outperforms the other classic algorithms in terms of connectivity, average remaining edges, sensitivity and monotonicity.

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Identification of key recovering node for spatial networks

Cited by 3 publications

References 40 publications

CRB: A new rumor blocking algorithm in online social networks based on competitive spreading model and influence maximization

CRB: A new rumor blocking algorithm in online social networks based on competitive spreading model and influence maximization

Source localization in signed networks with effective distance

Key node identification for a network topology using hierarchical comprehensive importance coefficients

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