Effective immunization strategy for rumor propagation based on maximum spanning tree

2014 ◽  
Author(s):  
Yuanyuan Bao ◽  
Yan Niu ◽  
Chengqi Yi ◽  
Yibo Xue
Keyword(s):  
Spanning Tree ◽  
Immunization Strategy ◽  
Rumor Propagation ◽  
Maximum Spanning Tree ◽  
Effective Immunization

scholarly journals Penerapan Bayesian Network dalam Memodelkan Kondisi Ekonomi Hijau Indonesia di Era Pandemi Berdasarkan Big Data

2021 ◽  
Vol 2021 (1) ◽  
pp. 1054-1064
Author(s):  
Salwa Rizqina Putri ◽  
Thosan Girisona Suganda ◽  
Setia Pramana
Keyword(s):  
Big Data ◽  
Bayesian Network ◽  
Data Model ◽  
Spanning Tree ◽  
Cross Validation ◽  
Parameter Learning ◽  
Maximum Spanning Tree

Untuk mendukung pertumbuhan ekonomi hijau Indonesia, diperlukan analisis lebih lanjut terkait aktivitas ekonomi di masa pandemi dan keterkaitannya dengan kondisi lingkungan. Penelitian ini bertujuan untuk menerapkan pendekatan Bayesian Network dalam memodelkan kondisi ekonomi hijau Indonesia di masa pandemi berdasarkan variabel-variabel yang disinyalir dapat berpengaruh seperti aktivitas ekonomi, kualitas udara, tingkat mobilitas penduduk, dan kasus positif COVID-19 yang diperoleh melalui big data. Model Bayesian Network yang dikonstruksi secara manual dengan algoritma Maximum Spanning Tree dipilih sebagai model terbaik dengan rata-rata akurasi 5-cross validation dalam memprediksi empat kelas PDRB sebesar 0,83. Model terbaik yang dipilih menunjukkan bahwa kondisi ekonomi Indonesia di era pandemi secara langsung dipengaruhi oleh intensitas cahaya malam (NTL) yang menunjukkan aktivitas ekonomi, kualitas udara (AQI), dan kasus positif COVID-19. Analisis parameter learning menunjukkan bahwa pertumbuhan ekonomi provinsi-provinsi Indonesia masih cenderung belum sejalan dengan terpeliharanya kualitas udara sehingga usaha untuk mencapai kondisi ekonomi hijau masih harus ditingkatkan.

scholarly journals Graphical Optimization of Spectral Shift Reconstructions for Optical Backscatter Reflectometry

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6154
Author(s):  
Daniel C. Sweeney ◽  
Dennis M. Sweeney ◽  
Christian M. Petrie
Keyword(s):  
Adaptive Algorithm ◽  
Spanning Tree ◽  
Mechanical Strain ◽  
Adaptive Algorithms ◽  
Spectral Shift ◽  
Reference Measurement ◽  
Interferometric Technique ◽  
Quality Metric ◽  
Spectral Shifts ◽  
Maximum Spanning Tree

Optical backscatter reflectometry (OBR) is an interferometric technique that can be used to measure local changes in temperature and mechanical strain based on spectral analyses of backscattered light from a singlemode optical fiber. The technique uses Fourier analyses to resolve spectra resulting from reflections occurring over a discrete region along the fiber. These spectra are cross-correlated with reference spectra to calculate the relative spectral shifts between measurements. The maximum of the cross-correlated spectra—termed quality—is a metric that quantifies the degree of correlation between the two measurements. Recently, this quality metric was incorporated into an adaptive algorithm to (1) selectively vary the reference measurement until the quality exceeds a predefined threshold and (2) calculate incremental spectral shifts that can be summed to determine the spectral shift relative to the initial reference. Using a graphical (network) framework, this effort demonstrated the optimal reconstruction of distributed OBR measurements for all sensing locations using a maximum spanning tree (MST). By allowing the reference to vary as a function of both time and sensing location, the MST and other adaptive algorithms could resolve spectral shifts at some locations, even if others can no longer be resolved.

The research on propagation modeling and governance strategies of online rumors based on behavior–attitude

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hailiang Chen ◽  
Chuan Ai ◽  
Bin Chen ◽  
Yong Zhao ◽  
Kaisheng Lai ◽  
...  
Keyword(s):  
Early Stage ◽  
Small World ◽  
Propagation Model ◽  
Online Data ◽  
Immunization Strategy ◽  
Content Type ◽  
Scale Free ◽  
Effective Governance ◽  
Human Behaviors ◽  
Rumor Propagation

PurposeThe purpose of this paper is to achieve effective governance of online rumors through the proposed rumor propagation model and immunization strategy.Design/methodology/approachThe paper leverages the agent-based modeling (ABM) method to model individuals from two aspects, behavior and attitude. Based on the analysis and research of online data, we propose a rumor propagation model, namely the Untouched view transmit removed-Susceptible hesitate agree disagree (Unite-Shad), and devise an immunization strategy, namely the Gravity Immunization Strategy (GIS). A graph-based framework, namely Pregel, is used to carry out the rumor propagation simulation experiments. Through the experiments, the rationality of the Unite-Shad and the effectiveness of the GIS are verified.FindingsThe study discovers that the inconsistency between human behaviors and attitudes in rumor propagation can be explained by the Unite-shad model. Besides, the GIS, which shows better performance in small-world networks than in scale-free networks, can effectively suppress rumor propagation in the early stage.Research limitations/implicationsThis paper provides an effective immunization strategy for rumor governance. Specifically, the Unite-Shad model reveals the mechanism of rumor propagation, and the GIS provides an effective governance method for selecting immune nodes.Originality/valueThe inconsistency of human behaviors and attitudes in real scenes is modeled in the Unite-Shad model. Combined with the model, the definition of diffusion domain is proposed and a novel immunization strategy, namely GIS, is designed, which is significant for the social governance of rumor propagation.

scholarly journals On the approximability of some maximum spanning tree problems

1997 ◽  
Vol 181 (1) ◽  
pp. 107-118 ◽  
Author(s):  
Giulia Galbiati ◽  
Angelo Morzenti ◽  
Francesco Maffioli
Keyword(s):  
Spanning Tree ◽  
Maximum Spanning Tree

scholarly journals Similarity Graph-Based Camera Tracking for Effective 3D Geometry Reconstruction with Mobile RGB-D Camera

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4897
Author(s):  
Jaepung An ◽  
Sangbeom Lee ◽  
Sanghun Park ◽  
Insung Ihm
Keyword(s):  
3D Reconstruction ◽  
Spanning Tree ◽  
Weighted Graph ◽  
Camera Tracking ◽  
Basic Algorithm ◽  
Novel Approach ◽  
3D Geometry ◽  
Processing Order ◽  
Maximum Spanning Tree ◽  
Similarity Graph

In this paper, we present a novel approach for reconstructing 3D geometry from a stream of images captured by a consumer-grade mobile RGB-D sensor. In contrast to previous real-time online approaches that process each incoming image in acquisition order, we show that applying a carefully selected order of (possibly a subset of) frames for pose estimation enables the performance of robust 3D reconstruction while automatically filtering out error-prone images. Our algorithm first organizes the input frames into a weighted graph called the similarity graph. A maximum spanning tree is then found in the graph, and its traversal determines the frames and their processing order. The basic algorithm is then extended by locally repairing the original spanning tree and merging disconnected tree components, if they exist, as much as possible, enhancing the result of 3D reconstruction. The capability of our method to generate a less error-prone stream from an input RGB-D stream may also be effectively combined with more sophisticated state-of-the-art techniques, which further increases their effectiveness in 3D reconstruction.

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