Localization of diffusion sources in complex networks: A maximum-largest method

Hu, Zhao-Long; Shen, Zhesi; Han, Jianmin; Peng, Hao; Lu, Jianfeng; Jia, Riheng; Zhu, Xiangbin; Zhao, Dandan

doi:10.1016/j.physa.2019.121262

Cited by 11 publications

(8 citation statements)

References 37 publications

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“…Third, time-line correlation should be introduced into topic-level sub-event development trends [41]. Lastly, the approach of network reconstruction [42,43,44] can be integrated into content reconstruction.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Reconstruction of Unfolding Sub-Events From Social Media Posts

Guo

Zhang

et al. 2022

Front. Phys.

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Event detection plays a crucial role in social media analysis, which usually concludes sub-event detection and correlation. In this article, we present a method for reconstructing the unfolding sub-event relations in terms of external expert knowledge. First, a Single Pass Clustering method is utilized to summarize massive social media posts. Second, a Label Propagation Algorithm is introduced to detect the sub-event according to the expert labeling. Third, a Word Mover’s Distance method is used to measure the correlation between the relevant sub-events. Finally, the Markov Chain Monte Carlo simulation method is presented to regenerate the popularity of social media posts. The experimental results show that the popularity dynamic of the empirical social media sub-events is consistent with the data generated by the proposed method. The evaluation of the unfolding model is 50.52% ∼ 88% higher than that of the random null model in the case of “Shanghai Tesla self-ignition incident.” This work is helpful for understanding the popularity mechanism of the unfolding events for online social media.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Reconstruction of Unfolding Sub-Events From Social Media Posts

Guo

Zhang

et al. 2022

Front. Phys.

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“…They employed a k-center method to identify multiple sources and the corresponding infection areas in general networks. [14] Other methods have also been proposed for source localization, including maximum likelihood estimation, [11,[15][16][17] statistical physics, [18][19][20][21] reverse propagation, [22][23][24][25][26][27][28][29][30][31][32][33][34] machine learning, [35,36] etc. [37][38][39] However, existing source localization research overlooks the signed nature of node connections, which is frequently encountered in signed networks, such as social networks that incorporate friend and enemy relationships.…”

Section: Introductionmentioning

confidence: 99%

“…[24] Building upon this work, Hu et al observed that the initial propagation time of the true source occurs at its maximum frequency in the referred initial propagation time matrix, leading to the proposal of a maximum-maximum source localization method. [30] Integer programming methods can improve accuracy, but observer selection remains random. [28] Nevertheless, the random observation method is still adopted, requiring a relatively large number of observers.…”

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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“…To overcome this limitation, researchers proposed to identify the sources based on limited observation (a) E-mail: yuanshanxiaowu@qq.com (b) E-mail: wangchao@xidian.edu.cn (corresponding author) [12,[20][21][22][23]. Specifically, several nodes on a network are selected as the observation nodes in the diffusion process, which can memorize the diffusion information, like the infected time.…”

mentioning

confidence: 99%

“…Fu et al [21] presented a novel backward diffusion-based method entitled the maximumminimum method (MMM) to locate sources with partial observations. But the accuracy of the MMM is susceptible to extreme values, which means the performance of the MMM may be undesirable when given a small fraction of observers [22]. Hu et al proposed the maximum largest method (MLM) which extends the MMM by changing the identifying strategy [22].…”

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confidence: 99%

Multi-source localization on complex networks based on community detection

Yuan¹,

Liu²,

Zeng³

et al. 2023

EPL

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Source localization is a crucial task on complex networks to understand the transmission mechanism and eliminate the malignant influence of diffusion processes. The previous works mainly detect multiple sources from a global perspective within a network. In this paper, we introduce community detection to source localization, aiming to address source localization from a local view. Specifically, a community detection-based multi-source localization method CDSL is proposed. Experiments on both several real-world and synthetic networks demonstrate that CDSL has higher accuracy and needs less runtime than the existing source localization methods. We also explore how different observation selection strategies affect the performance of CDSL and find out that selecting the nodes with a small degree as observers performs best.

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Localization of diffusion sources in complex networks: A maximum-largest method

Cited by 11 publications

References 37 publications

Reconstruction of Unfolding Sub-Events From Social Media Posts

Reconstruction of Unfolding Sub-Events From Social Media Posts

Source localization in signed networks with effective distance

Multi-source localization on complex networks based on community detection

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