Maximum likelihood rumor source detection in a star network

Spencer, Sam; Srikant, R.

doi:10.1109/icassp.2016.7472067

Cited by 12 publications

(7 citation statements)

References 5 publications

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“…For the situations described by our model, we define a computationally-efficient approximation to the maximum likelihood estimate (MLE) of the hyperedge containing the source of the infection relative to the particular infected set in an observed snapshot of data. We then prove the approximation is close to MLE (in the sense of [16]), and also give simulations demonstrating efficacy of the estimator.…”

Section: Introductionmentioning

confidence: 77%

“…Therefore, we assume that any nontrivial infection pattern will comprise O, along with the closest k i nodes along each arm i. For an extended star network, there is a simple, closed-form expression for a highly accurate approximation to the MLE [16]. It is, along the longest arm:…”

Section: Source Localization For Graphsmentioning

confidence: 99%

“…Since infection source localization in general networks is mathematically difficult, one must make some simplifying assumptions to define a more tractable problem. In our case, we extend the model of susceptible-infected (SI) spreading over extended star networks [16] to an analogous class of extended star hypertrees. For the situations described by our model, we define a computationally-efficient approximation to the maximum likelihood estimate (MLE) of the hyperedge containing the source of the infection relative to the particular infected set in an observed snapshot of data.…”

Section: Introductionmentioning

confidence: 99%

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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: Introductionmentioning

confidence: 77%

Section: Source Localization For Graphsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Spencer

Varshney

2020

Preprint

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“…Recent literature reviews [32][33][34][35] highlighted the need of designing and developing real-time solutions to deal with false information, and performing early fake news detection. Indeed, researchers already focused on the detection of rumors and rumor sources in networks and social media environments [36][37][38][39][40][41][42], however in this work we introduce a framework for a timely identification of possible, future fake news topics. To our knowledge, this is the first attempt in that direction, although early warning systems have already been explored for different applications, such as the problem of detecting vandals on Wikipedia [43], or the timely identification of adverse drug reactions [44].…”

Section: Related Workmentioning

confidence: 99%

Polarization and Fake News

et al. 2019

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Users’ polarization and confirmation bias play a key role in misinformation spreading on online social media. Our aim is to use this information to determine in advance potential targets for hoaxes and fake news. In this article, we introduce a framework for promptly identifying polarizing content on social media and, thus, “predicting” future fake news topics. We validate the performances of the proposed methodology on a massive Italian Facebook dataset, showing that we are able to identify topics that are susceptible to misinformation with 77% accuracy. Moreover, such information may be embedded as a new feature in an additional classifier able to recognize fake news with 91% accuracy. The novelty of our approach consists in taking into account a series of characteristics related to users’ behavior on online social media such as Facebook, making a first, important step towards the mitigation of misinformation phenomena by supporting the identification of potential misinformation targets and thus the design of tailored counter-narratives.

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“…A network centrality called the rumor centrality was introduced in [7] to solve optimally special instances of the problem when the graph topology of the online social networks is assumed to be degree-regular tree with countably infinite number of vertices and assuming a SI (susceptible-infectious) spreading model. This problem was subsequently extended to various problem settings, e.g., extension in [8] to random increasing trees, extension in [9] to probabilistic sampling of the rumor graph, extension in [10] to star graph, extension in [11], [12] to the scenario of multiple sources, extension in [13] to multiple observations. The authors in [14] proposed a Markov chain Monte Carlo based algorithms, and the authors in [15] proposed message-passing algorithms based on probabilistic analysis of graph boundaries.…”

Section: Introductionmentioning

confidence: 99%

Epidemic Source Detection in Contact Tracing Networks: Epidemic Centrality in Graphs and Message-Passing Algorithms

Yu,

Tan,

2022

Preprint

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We study the epidemic source detection problem in contact tracing networks modeled as a graph-constrained maximum likelihood estimation problem using the susceptible-infected model in epidemiology. Based on a snapshot observation of the infection subgraph, we first study finite degree regular graphs and regular graphs with cycles separately, thereby establishing a mathematical equivalence in maximal likelihood ratio between the case of finite acyclic graphs and that of cyclic graphs. In particular, we show that the optimal solution of the maximum likelihood estimator can be refined to distances on graphs based on a novel statistical distance centrality that captures the optimality of the nonconvex problem. An efficient contact tracing algorithm is then proposed to solve the general case of finite degree-regular graphs with multiple cycles. Our performance evaluation on a variety of graphs shows that our algorithms outperform the existing state-of-the-art heuristics using contact tracing data from the SARS-CoV 2003 and COVID-19 pandemics by correctly identifying the superspreaders on some of the largest superspreading infection clusters in Singapore and Taiwan.

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Maximum likelihood rumor source detection in a star network

Cited by 12 publications

References 5 publications

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

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

Polarization and Fake News

Epidemic Source Detection in Contact Tracing Networks: Epidemic Centrality in Graphs and Message-Passing Algorithms

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