An official website of the United States government
Information and content can spread in social networks analogous to how diseases spread between organisms. Identifying the source of an outbreak is challenging when the infection times are unknown. We consider the problem of detecting the source of a rumor that spread randomly in a network according to a simple diffusion model, the susceptible-infected (SI) exponential time model. The infection times are unknown. Only the set of nodes that propagated the rumor before a certain time is known. Since evaluating the likelihood of spreads is computationally prohibitive, we propose a simple and efficient procedure to approximate the likelihood and select a candidate rumor source. We empirically demonstrate our method out-performs the Jordan center procedure in various random graphs and a real-world network.
more »
« less
A Greedy Monitoring Station Selection for Rumor Source Detection in Online Social Networks
In monitoring station observation, for the best accuracy of rumor source detection, it is important to deploy monitors appropriately into the network. There are, however, a very limited number of studies on the monitoring station selection. This article will study the problem of detecting a single rumormonger based on an observation of selected infection monitoring stations in a complete snapshot taken at some time in an online social network (OSN) following the independent cascade (IC) model. To deploy monitoring stations into the observed network, we propose an influence-distancebased k-station selection method where the influence distance is a conceptual measurement that estimates the probability that a rumor-infected node can influence its uninfected neighbors. Accordingly, a greedy algorithm is developed to find the best k monitoring stations among all rumor-infected nodes with a 2-approximation. Based on the infection path, which is most likely toward the k infection monitoring stations, we derive that an estimator for the “most like” rumor source under the IC model is the Jordan infection center in a graph. Our theoretical analysis is presented in the article. The effectiveness of our method is verified through experiments over both synthetic and real-world datasets. As shown in the results, our k-station selection method outperforms off-the-shelf methods in most cases in the network under the IC model.
more »
« less
- Award ID(s):
- 1822985
- PAR ID:
- 10545741
- Publisher / Repository:
- IEEE Publisher
- Date Published:
- Journal Name:
- IEEE Transactions on Computational Social Systems
- Volume:
- 11
- Issue:
- 2
- ISSN:
- 2373-7476
- Page Range / eLocation ID:
- 2644 - 2655
- Subject(s) / Keyword(s):
- Independent cascade (IC) model, influence distance, monitor deployment, rumor source detection
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
ABSTRACT The recorded seismic waveform is a convolution of event source term, path term, and station term. Removing high-frequency attenuation due to path effect is a challenging problem. Empirical Green’s function (EGF) method uses nearly collocated small earthquakes to correct the path and station terms for larger events recorded at the same station. However, this method is subject to variability due to many factors. We focus on three events that were well recorded by the seismic network and a rapid response distributed acoustic sensing (DAS) array. Using a suite of high-quality EGF events, we assess the influence of time window, spectral measurement options, and types of data on the spectral ratio and relative source time function (RSTF) results. Increased number of tapers (from 2 to 16) tends to increase the measured corner frequency and reduce the source complexity. Extended long time window (e.g., 30 s) tends to produce larger variability of corner frequency. The multitaper algorithm that simultaneously optimizes both target and EGF spectra produces the most stable corner-frequency measurements. The stacked spectral ratio and RSTF from the DAS array are more stable than two nearby seismic stations, and are comparable to stacked results from the seismic network, suggesting that DAS array has strong potential in source characterization.more » « less
-
Abstract ObjectivesUnderstanding disease transmission is a fundamental challenge in ecology. We used transmission potential networks to investigate whether a gastrointestinal protozoan (Blastocystisspp.) is spread through social, environmental, and/or zoonotic pathways in rural northeast Madagascar. Materials and MethodsWe obtained survey data, household GPS coordinates, and fecal samples from 804 participants. Surveys inquired about social contacts, agricultural activity, and sociodemographic characteristics. Fecal samples were screened forBlastocystisusing DNA metabarcoding. We also tested 133 domesticated animals forBlastocystis. We used network autocorrelation models and permutation tests (networkk‐test) to determine whether networks reflecting different transmission pathways predicted infection. ResultsWe identified six distinctBlastocystissubtypes among study participants and their domesticated animals. Among the 804 human participants, 74% (n = 598) were positive for at least oneBlastocystissubtype. Close proximity to infected households was the most informative predictor of infection with any subtype (model averaged OR [95% CI]: 1.56 [1.33–1.82]), and spending free time with infected participants was not an informative predictor of infection (model averaged OR [95% CI]: 0.95 [0.82–1.10]). No human participant was infected with the same subtype as the domesticated animals they owned. DiscussionOur findings suggest thatBlastocystisis most likely spread through environmental pathways within villages, rather than through social or animal contact. The most likely mechanisms involve fecal contamination of the environment by infected individuals or shared food and water sources. These findings shed new light on human‐pathogen ecology and mechanisms for reducing disease transmission in rural, low‐income settings.more » « less
-
SUMMARY Traditional two-station ambient noise interferometry estimates the Green’s function between a pair of synchronously deployed seismic stations. Three-station interferometry considers records observed three stations at a time, where two of the stations are considered receiver–stations and the third is a source–station. Cross-correlations between records at the source–station with each of the receiver–stations are correlated or convolved again to estimate the Green’s function between the receiver–stations, which may be deployed asynchronously. We use data from the EarthScope USArray in the western United States to compare Rayleigh wave dispersion obtained from two-station and three-station interferometry. Three three-station interferometric methods are distinguished by the data segment utilized (coda-wave or direct-wave) and whether the source–stations are constrained to lie in stationary phase zones approximately inline with the receiver–stations. The primary finding is that the three-station direct wave methods perform considerably better than the three-station coda-wave method and two-station ambient noise interferometry for obtaining surface wave dispersion measurements in terms of signal-to-noise ratio, bandwidth, and the number of measurements obtained, but possess small biases relative to two-station interferometry. We present a ray-theoretic correction method that largely removes the bias below 40 s period and reduces it at longer periods. Three-station direct-wave interferometry provides substantial value for imaging the crust and uppermost mantle, and its ability to bridge asynchronously deployed stations may impact the design of seismic networks in the future.more » « less
-
null (Ed.)Online social networks provide a convenient platform for the spread of rumors, which could lead to serious aftermaths such as economic losses and public panic. The classical rumor blocking problem aims to launch a set of nodes as a positive cascade to compete with misinformation in order to limit the spread of rumors. However, most of the related researches were based on a one-dimensional diffusion model. In reality, there is more than one feature associated with an object. A user’s impression on this object is determined not just by one feature but by her overall evaluation of all features associated with it. Thus, the influence spread of this object can be decomposed into the spread of multiple features. Based on that, we design a multi-feature diffusion model (MF-model) in this paper and formulate a multi-feature rumor blocking (MFRB) problem on a multi-layer network structure according to this model. To solve the MFRB problem, we design a creative sampling method called Multi-Sampling, which can be applied to this multi-layer network structure. Then, we propose a Revised-IMM algorithm and obtain a satisfactory approximate solution to MFRB. Finally, we evaluate our proposed algorithm by conducting experiments on real datasets, which shows the effectiveness of our Revised- IMM and its advantage to their baseline algorithms.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TCSS.2023.3284909
