Locating the Source of Diffusion in Complex Networks via Gaussian-Based Localization and Deduction

Li, Xiang; Wang, Xiaojie; Zhao, Chengli; Zhang, Xue; Yi, Dongyun

doi:10.3390/app9183758

Cited by 15 publications

(5 citation statements)

References 37 publications

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“…On similar note there has been some impressive work by Zejnilovic et al [36][37][38], Wang [23], Fang et al [39], Li et al [29] and Shi et al [40] where new localization methods are introduced and several methods for sensor placement are studied. Nevertheless, the localization scheme and, most importantly, certain assumptions are different than the ones we are using in this paper and therefore we will omit those as well.…”

Section: Related Workmentioning

confidence: 95%

Optimizing sensors placement in complex networks for localization of hidden signal source: A review

Paluch

Gajewski

Hołyst

et al. 2020

Future Generation Computer Systems

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As the world becomes more and more interconnected, our everyday objects become part of the Internet of Things, and our lives get more and more mirrored in virtual reality, where every piece of information, including misinformation, fake news and malware, can spread very fast practically anonymously. To suppress such uncontrolled spread, efficient computer systems and algorithms capable to track down such malicious information spread have to be developed. Currently, the most effective methods for source localization are based on sensors which provide the times at which they detect the spread. We investigate the problem of the optimal placement of such sensors in complex networks and propose a new graph measure, called Collective Betweenness, which we compare against four other metrics. Extensive numerical tests are performed on different types of complex networks over the wide ranges of densities of sensors and stochasticities of signal. In these tests, we discovered clear difference in comparative performance of the investigated optimal placement methods between real or scale-free synthetic networks versus narrow degree distribution networks. The former have a clear region for any given method's dominance in contrast to the latter where the performance maps are less homogeneous. We find that while choosing the best method is very network and spread dependent, there are two methods that consistently stand out. High Variance Observers seem to do very well for spread with low stochasticity whereas Collective Betwenness, introduced in this paper, thrives when the spread is highly unpredictable.

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Section: Related Workmentioning

confidence: 95%

Optimizing sensors placement in complex networks for localization of hidden signal source: A review

Paluch

Gajewski

Hołyst

et al. 2020

Future Generation Computer Systems

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show abstract

“…[28] Nevertheless, the random observation method is still adopted, requiring a relatively large number of observers. Additionally, researchers have explored the optimal placement problem of observers in complex networks, including betweenness centrality (BC), [47] high coverage, [48] and other approaches. [49] The observation theory can be adopted to achieve the minimum number of observers, but only for linear diffusion systems.…”

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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“…In the last decade, there have been numerous advances in the field of locating sources of diffusion in complex networks 1 – 35 . Now, perhaps more than ever, such studies are relevant to the global challenges our societies face.…”

Section: Introductionmentioning

confidence: 99%

Comparison of observer based methods for source localisation in complex networks

Gajewski

Paluch

Suchecki

et al. 2022

Sci Rep

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In recent years, research on methods for locating a source of spreading phenomena in complex networks has seen numerous advances. Such methods can be applied not only to searching for the “patient zero” in epidemics, but also finding the true sources of false or malicious messages circulating in the online social networks. Many methods for solving this problem have been established and tested in various circumstances. Yet, we still lack reviews that would include a direct comparison of efficiency of these methods. In this paper, we provide a thorough comparison of several observer-based methods for source localisation on complex networks. All methods use information about the exact time of spread arrival at a pre-selected group of vertices called observers. We investigate how the precision of the studied methods depends on the network topology, density of observers, infection rate, and observers’ placement strategy. The direct comparison between methods allows for an informed choice of the methods for applications or further research. We find that the Pearson correlation based method and the method based on the analysis of multiple paths are the most effective in networks with synthetic or real topologies. The former method dominates when the infection rate is low; otherwise, the latter method takes over.

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Locating the Source of Diffusion in Complex Networks via Gaussian-Based Localization and Deduction

Cited by 15 publications

References 37 publications

Optimizing sensors placement in complex networks for localization of hidden signal source: A review

Optimizing sensors placement in complex networks for localization of hidden signal source: A review

Source localization in signed networks with effective distance

Comparison of observer based methods for source localisation in complex networks

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