How to Identify an Infection Source With Limited Observations
TL;DR: This work considers the problem of estimating an infection source for a Susceptible-Infected model, and shows that an estimator for the source node associated with the most likely infection path that yields the limited observations is given by a Jordan center.
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Abstract: A rumor spreading in a social network or a disease propagating in a community can be modeled as an infection spreading in a network. Finding the infection source is a challenging problem, which is made more difficult in many applications where we have access only to a limited set of observations. We consider the problem of estimating an infection source for a Susceptible-Infected model, in which not all infected nodes can be observed. When the network is a tree, we show that an estimator for the source node associated with the most likely infection path that yields the limited observations is given by a Jordan center, i.e., a node with minimum distance to the set of observed infected nodes. We also propose approximate source estimators for general networks. Simulation results on various synthetic networks and real world networks suggest that our estimators perform better than distance, closeness, and betweenness centrality based heuristics .
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Citations
On the universality of the Jordan center for estimating the rumor source in a social network
Wuqiong Luo,Wee Peng Tay,Mei Leng,Maria Katrina Guevara +3 more
- 21 Jul 2015
TL;DR: The Jordan center is an optimal rumor source estimator under the most likely infection path criterion for a wide range of spreading parameters where nodes may have different infection, recovery and reinfection rates.
14
Back To The Source: An Online Approach for Sensor Placement and Source Localization
Brunella Spinelli,L. Elisa Celis,Patrick Thiran +2 more
- 03 Apr 2017
TL;DR: In this paper, the authors propose an online approach to source localization, which deploys a priori only a small number of sensors (which reveal if they are reached by an infection) and then iteratively chooses the best location to place a new sensor in order to localize the source.
Towards Anomalous Diffusion Sources Detection in a Large Network
TL;DR: This article presents a new regression learning model that can detect anomalous diffusion sources by jointly solving five challenges, that is, unknown number of source nodes, few activated detectors, unknown initial propagation time, uncertain propagation path and uncertain propagation time delay.
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Budgeted sensor placement for source localization on trees
L. Elisa Celis,Filip Pavetic,Brunella Spinelli,Patrick Thiran +3 more
- 01 Dec 2015
TL;DR: A notion of vertex resolvability is introduced, which gives polynomial time algorithms for both finding the sensors that maximize the probability of correct detection of the source and for identifying the sensor set that minimizes the expected distance between the real source and the estimated one.
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Observer Placement for Source Localization: The Effect of Budgets and Transmission Variance
TL;DR: This work develops a principled approach for addressing the problem even when transmission delays are random and shows that the optimal observer-placement differs depending on the variance of the transmission delays and proposes approaches in both low- and high-variance settings.
13
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