Identification and Asymptotic Localization of Rumor Sources Using the Method of Types

Kesavareddigari, Himaja; Spencer, Sam; Eryılmaz, Atilla; Srikant, R.

doi:10.1109/tnse.2019.2911275

Cited by 15 publications

(3 citation statements)

References 27 publications

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“…Further analysis in [18] tightened the approximation bound for this estimator. Note that estimator (1) is scale-invariantall arm lengths k i can be scaled by a constant, and the resulting value of ℓ will be scaled by the same constant.…”

Section: Source Localization For Graphsmentioning

confidence: 96%

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Social Bubbles and Superspreaders: Source Identification for Contagion Processes on Hypertrees

Spencer

Varshney

2020

Preprint

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Previous work has shown that for contagion processes on extended star networks (trees with exactly one node of degree > 2), there is a simple, closed-form expression for a highly accurate approximation to the maximum likelihood infection source. Here, we generalize that result to a class of hypertrees which, although somewhat structurally analogous, provides a much richer representation space. In particular, this approach can be used to estimate patient zero sources, even when the infection has been propagated via large group gatherings rather than person-to-person spread, and when it is spreading through interrelated social bubbles with varying degrees of overlap. In contact tracing contexts, this estimator may be used to identify the source of a local outbreak, which can then be used for forward tracing or for further backward tracing (by similar or other means) to an upstream source.

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Section: Source Localization For Graphsmentioning

confidence: 96%

“…The number of arms is random (2-6) as before, but now all arms but one have random length (11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), while a randomly chosen arm has random length (31-50). The overlap sizes are randomly selected (1-6).…”

Section: Constrainedmentioning

confidence: 99%

Social Bubbles and Superspreaders: Source Identification for Contagion Processes on Hypertrees

Spencer

Varshney

2020

Preprint

Self Cite

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“…Reference [28] considered the singlesource inference problem of the heterogeneous infection time distribution under the SI model (that is, the infection time distribution of each node is different). In reference [29], the authors proposed a rumor source identification method based on the center of the type, which offers a highly efficient source identification with logarithmic approximation error in large networks.…”

Section: Related Workmentioning

confidence: 99%

Online Social Network Information Source Identification Algorithm Based on Multi-Attribute Topological Clustering

Dong,

Lu,

Tan

et al. 2024

Electronics

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This paper focuses on the problem of information source identification in online social networks (OSNs). By analyzing the research situation of source identification problems and challenges (such as the randomness of the information dissemination process and complexity of the underlying network topology), this paper studies the problem of multiple source diffusion and proposes a source identification algorithm based on multi-attribute topological clustering (MaTC). The basic idea of the algorithm is to decompose the multi-source problems into a series of single-source problems by using clustering partitioning to improve accuracy and efficiency. Firstly, it estimates the number of source nodes, which is also the number of network partitions, then characterizes the combination of multiple attribute structures as an attribute index of topological clustering, performs an analysis of the distribution of real source nodes in each partition to evaluate the accuracy of the clustering partition, and finally uses Jordan centrality within each partition for single-source identification. Through comparative experiments, it is verified that the proposed MaTC algorithm is superior to the comparison algorithms in evaluating indicators.

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BPSL: a new rumor source location algorithm based on the time-stamp back propagation in social networks

2021

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Identification and Asymptotic Localization of Rumor Sources Using the Method of Types

Cited by 15 publications

References 27 publications

Social Bubbles and Superspreaders: Source Identification for Contagion Processes on Hypertrees

Social Bubbles and Superspreaders: Source Identification for Contagion Processes on Hypertrees

Online Social Network Information Source Identification Algorithm Based on Multi-Attribute Topological Clustering

BPSL: a new rumor source location algorithm based on the time-stamp back propagation in social networks

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