Dataset Information

Community-Based Event Detection in Temporal Networks.

ABSTRACT: We propose a method for detecting large events based on the structure of temporal communication networks. Our method is motivated by findings that viral information spreading has distinct diffusion patterns with respect to community structure. Namely, we hypothesize that global events trigger viral information cascades that easily cross community boundaries and can thus be detected by monitoring intra- and inter-community communications. By comparing the amount of communication within and across communities, we show that it is possible to detect events, even when they do not trigger a significantly larger communication volume. We demonstrate the effectiveness of our method using two examples-the email communication network of Enron and the Twitter communication network during the Boston Marathon bombing.

SUBMITTER: Moriano P

PROVIDER: S-EPMC6416296 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Publications

Community-Based Event Detection in Temporal Networks.

Moriano Pablo P Finke Jorge J Ahn Yong-Yeol YY

Scientific reports 20190313 1

We propose a method for detecting large events based on the structure of temporal communication networks. Our method is motivated by findings that viral information spreading has distinct diffusion patterns with respect to community structure. Namely, we hypothesize that global events trigger viral information cascades that easily cross community boundaries and can thus be detected by monitoring intra- and inter-community communications. By comparing the amount of communication within and across ...[more]

PMID: 30867459

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Project description:BackgroundLike many developing countries, Mali has few sources of mortality data. High quality mortality estimates are available from household surveys, such as the demographic and health surveys (DHS), approximately every five years, making it difficult to track progress in reducing mortality. The Rapid Mortality Monitoring (RMM) project in Mali aimed to address this issue by testing a community-based approach to measuring under-five mortality on a yearly basis.Methods and findingsSeventy-eight community-based workers (relais) were identified in 20 villages comprising approximately 5,300 households. The relais reported pregnancies, births, and under-five deaths from July, 2012 to November, 2013. Data were double-entered, reconciled, cleaned, and analyzed monthly. In November-December 2013, we administered a full pregnancy history (FPH) to women of reproductive age in a census of the households in the project villages. We assessed the completeness of the counts of births and deaths, and the validity of under-five, infant, and neonatal mortality rates from the community-based method against the retrospective FPH for two rolling twelve-month periods. Monthly reporting by relais was high, with reports on pregnancies, births, and deaths consistently provided from all 78 relais catchment areas. Relais reported 1,660 live births and 276 under-five deaths from July, 2012 to November, 2013. The under-five mortality rate calculated from the relais data was similar to that estimated using the validation survey, where the overall ratios of the community-based to FPH-based mortality rates for the reporting periods were 100.4 (95% CI: 80.4, 120.5) and 100.8 (95% CI: 79.5, 122.0).ConclusionsOn a small scale, the community-based method in Mali produced estimates of annualized under-five mortality rates that were consistent with those obtained from a FPH. The community-based method should be considered for scale-up in Mali, with appropriate measures to ensure community engagement, data quality, and cross-validation with comparable FPHs.

Project description:Bioacoustic sensors, sometimes known as autonomous recording units (ARUs), can record sounds of wildlife over long periods of time in scalable and minimally invasive ways. Deriving per-species abundance estimates from these sensors requires detection, classification, and quantification of animal vocalizations as individual acoustic events. Yet, variability in ambient noise, both over time and across sensors, hinders the reliability of current automated systems for sound event detection (SED), such as convolutional neural networks (CNN) in the time-frequency domain. In this article, we develop, benchmark, and combine several machine listening techniques to improve the generalizability of SED models across heterogeneous acoustic environments. As a case study, we consider the problem of detecting avian flight calls from a ten-hour recording of nocturnal bird migration, recorded by a network of six ARUs in the presence of heterogeneous background noise. Starting from a CNN yielding state-of-the-art accuracy on this task, we introduce two noise adaptation techniques, respectively integrating short-term (60 ms) and long-term (30 min) context. First, we apply per-channel energy normalization (PCEN) in the time-frequency domain, which applies short-term automatic gain control to every subband in the mel-frequency spectrogram. Secondly, we replace the last dense layer in the network by a context-adaptive neural network (CA-NN) layer, i.e. an affine layer whose weights are dynamically adapted at prediction time by an auxiliary network taking long-term summary statistics of spectrotemporal features as input. We show that PCEN reduces temporal overfitting across dawn vs. dusk audio clips whereas context adaptation on PCEN-based summary statistics reduces spatial overfitting across sensor locations. Moreover, combining them yields state-of-the-art results that are unmatched by artificial data augmentation alone. We release a pre-trained version of our best performing system under the name of BirdVoxDetect, a ready-to-use detector of avian flight calls in field recordings.

Dataset Information

Community-Based Event Detection in Temporal Networks.

Publications

Community-Based Event Detection in Temporal Networks.

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