An Algorithmic Framework for Estimating Rumor Sources With Different Start Times

Ji, Feng; Tay, Wee Peng; Varshney, Lav R.

doi:10.1109/tsp.2017.2659643

Cited by 48 publications

(21 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering the heterogeneous SIR diffusion in the ER random graph, they proved that OJC can locate all sources with probability one asymptotically with partial observations. Ji et al [17] developed a theoretical framework to estimate rumor sources, given an observation of the infection graph and the number of rumor sources.…”

Section: Related Workmentioning

confidence: 99%

Estimating the Information Source under Decaying Diffusion Rates

Woo

Choi

2019

Electronics

View full text Add to dashboard Cite

Recently, as arising from online social network services such as Facebook and Twitter, people are more actively using social networks to exchange their new information. In this consideration, finding the information source becomes one of the indispensable and useful tasks in detecting a malicious agent and an influential person in the networks. A seminal work by Shah and Zaman in 2010 showed that the detection probability cannot be beyond 31% even for regular trees if time goes to infinity. From the study, extensive researches have been done for this problem, whose major interests lie in constructing an efficient estimator and providing theoretical analysis on its detection performance. However, most of the works assumed the homogeneous diffusion rate of the information, where the diffusion rate does not change at all times over the network. In practice, it is reported that information has a lifetime and it becomes less attractive over time. In this paper, we study the problem of detecting the information source when the diffusion rate decreases by the distance from the source in the network. As a result, we obtain analytical detection performance of Maximum Likelihood Estimator (MLE) and validate our theoretical findings over the regular tree, random and real-world networks.

show abstract

Section: Related Workmentioning

confidence: 99%

Estimating the Information Source under Decaying Diffusion Rates

Woo

Choi

2019

Electronics

View full text Add to dashboard Cite

show abstract

“…The results of [46] have been extended in many ways, e.g., to the case of multiple sources [10], [25], [35] or to a setting where (B.2) is replaced with an assumption similar to (A.2) [29]. An alternate line of work that also uses Assumption (B.1), allows the observed states to be noisy, i.e., potentially inaccurate.…”

Section: Related Workmentioning

confidence: 99%

A General Framework for Sensor Placement in Source Localization

Spinelli

Celis

Thiran

2019

IEEE Trans. Netw. Sci. Eng.

View full text Add to dashboard Cite

When an epidemic spreads in a given network of individuals or communities, can we detect its source using only the information provided by a small set of nodes? We propose a general framework that incorporates two dimensions. First, we can either rely exclusively on a set of selected nodes (i.e., sensors) which always reveal their state independently of any particular epidemic (these are called static), or we can add some sensors (called dynamic) as an epidemic spreads, depending on which additional information is required. Second, the method can either localizes the source after an epidemic has spread through the entire network (offline), or while the epidemic is ongoing (online). We empirically study the performance of offline and online localization both with and without dynamic sensors. Our analysis shows that, by using dynamic sensors, the number of sensors necessary to localize the source is reduced by up to a factor of 10 and that, even with high-variance transmission delays, the source can be localized by using fewer than 5% of the nodes as sensors.

show abstract

“…Specifically, in the SI model, an infected node remains infected forever; in the SIR model, it can recover and cannot be further infected; and in the SIS model, a recovered node can become infected again. A rumor centrality estimator under the SI model was developed in [9], [12], while [13]- [16] developed estimators for identifying multiple infection sources under the SI model. The paper [17] considers infection source estimation when only a subset of infected nodes are observed.…”

Section: A Related Workmentioning

confidence: 99%

Estimating Infection Sources in Networks Using Partial Timestamps

Tang

Tay

2018

IEEE Trans.Inform.Forensic Secur.

Self Cite

View full text Add to dashboard Cite

We study the problem of identifying infection sources in a network based on the network topology, and a subset of infection timestamps. In the case of a single infection source in a tree network, we derive the maximum likelihood estimator of the source and the unknown diffusion parameters. We then introduce a new heuristic involving an optimization over a parametrized family of Gromov matrices to develop a single source estimation algorithm for general graphs. Compared with the breadth-first search tree heuristic commonly adopted in the literature, simulations demonstrate that our approach achieves better estimation accuracy than several other benchmark algorithms, even though these require more information like the diffusion parameters. We next develop a multiple sources estimation algorithm for general graphs, which first partitions the graph into source candidate clusters, and then applies our single source estimation algorithm to each cluster. We show that if the graph is a tree, then each source candidate cluster contains at least one source. Simulations using synthetic and real networks, and experiments using real-world data suggest that our proposed algorithms are able to estimate the true infection source(s) to within a small number of hops with a small portion of the infection timestamps being observed.

show abstract

An Algorithmic Framework for Estimating Rumor Sources With Different Start Times

Cited by 48 publications

References 40 publications

Estimating the Information Source under Decaying Diffusion Rates

Estimating the Information Source under Decaying Diffusion Rates

A General Framework for Sensor Placement in Source Localization

Estimating Infection Sources in Networks Using Partial Timestamps

Contact Info

Product

Resources

About