scholarly journals Locating the Epidemic Source in Complex Networks with Sparse Observers

2019 ◽  
Vol 9 (18) ◽  
pp. 3644 ◽  
Author(s):  
Xiang Li ◽  
Xiaojie Wang ◽  
Chengli Zhao ◽  
Xue Zhang ◽  
Dongyun Yi
Keyword(s):  
Complex Networks ◽  
Source Localization ◽  
Disease Spread ◽  
Initial Time ◽  
Epidemic Spread ◽  
Localization Accuracy ◽  
General Network ◽  
Maximum Spanning Tree ◽  
Comparison Algorithms ◽  
Empirical Networks

Epidemic source localization is one of the most meaningful areas of research in complex networks, which helps solve the problem of infectious disease spread. Limited by incomplete information of nodes and inevitable randomness of the spread process, locating the epidemic source becomes a little difficult. In this paper, we propose an efficient algorithm via Bayesian Estimation to locate the epidemic source and find the initial time in complex networks with sparse observers. By modeling the infected time of observers, we put forward a valid epidemic source localization method for tree network and further extend it to the general network via maximum spanning tree. The numerical analyses in synthetic networks and empirical networks show that our algorithm has a higher source localization accuracy than other comparison algorithms. In particular, when the randomness of the spread path enhances, our algorithm has a better performance. We believe that our method can provide an effective reference for epidemic spread and source localization in complex networks.

scholarly journals Locating the Source of Diffusion in Complex Networks via Gaussian-Based Localization and Deduction

2019 ◽  
Vol 9 (18) ◽  
pp. 3758 ◽  
Author(s):  
Xiang Li ◽  
Xiaojie Wang ◽  
Chengli Zhao ◽  
Xue Zhang ◽  
Dongyun Yi
Keyword(s):  
Complex Networks ◽  
Source Localization ◽  
Information Diffusion ◽  
Large Scale ◽  
Computational Cost ◽  
Likelihood Estimation ◽  
Power Grids ◽  
Optimization Approach ◽  
Localization Accuracy ◽  
Diffusion Source

Locating the source that undergoes a diffusion-like process is a fundamental and challenging problem in complex network, which can help inhibit the outbreak of epidemics among humans, suppress the spread of rumors on the Internet, prevent cascading failures of power grids, etc. However, our ability to accurately locate the diffusion source is strictly limited by incomplete information of nodes and inevitable randomness of diffusion process. In this paper, we propose an efficient optimization approach via maximum likelihood estimation to locate the diffusion source in complex networks with limited observations. By modeling the informed times of the observers, we derive an optimal source localization solution for arbitrary trees and then extend it to general graphs via proper approximations. The numerical analyses on synthetic networks and real networks all indicate that our method is superior to several benchmark methods in terms of the average localization accuracy, high-precision localization and approximate area localization. In addition, low computational cost enables our method to be widely applied for the source localization problem in large-scale networks. We believe that our work can provide valuable insights on the interplay between information diffusion and source localization in complex networks.

scholarly journals Optimal localization of diffusion sources in complex networks

2017 ◽  
Vol 4 (4) ◽  
pp. 170091 ◽  
Author(s):  
Zhao-Long Hu ◽  
Xiao Han ◽  
Ying-Cheng Lai ◽  
Wen-Xu Wang
Keyword(s):  
Complex Networks ◽  
Compressive Sensing ◽  
Source Localization ◽  
High Efficiency ◽  
Signal Reconstruction ◽  
Arbitrary Distribution ◽  
Output Analysis ◽  
Undirected Network ◽  
Empirical Networks ◽  
Optimal Localization

Locating sources of diffusion and spreading from minimum data is a significant problem in network science with great applied values to the society. However, a general theoretical framework dealing with optimal source localization is lacking. Combining the controllability theory for complex networks and compressive sensing, we develop a framework with high efficiency and robustness for optimal source localization in arbitrary weighted networks with arbitrary distribution of sources. We offer a minimum output analysis to quantify the source locatability through a minimal number of messenger nodes that produce sufficient measurement for fully locating the sources. When the minimum messenger nodes are discerned, the problem of optimal source localization becomes one of sparse signal reconstruction, which can be solved using compressive sensing. Application of our framework to model and empirical networks demonstrates that sources in homogeneous and denser networks are more readily to be located. A surprising finding is that, for a connected undirected network with random link weights and weak noise, a single messenger node is sufficient for locating any number of sources. The framework deepens our understanding of the network source localization problem and offers efficient tools with broad applications.

scholarly journals Balancing capacity and epidemic spread in the global airline network

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Robert Harper ◽  
Philip Tee
Keyword(s):  
Complex Networks ◽  
Transportation Networks ◽  
Disease Spread ◽  
Epidemic Spread ◽  
Spread Rate ◽  
Transport Networks ◽  
Travel Restrictions ◽  
Airline Network ◽  
Control Disease ◽  
Optimum Balance

AbstractThe structure of complex networks has long been understood to play a role in transmission and spreading phenomena on a graph. Such networks form an important part of the structure of society, including transportation networks. As society fights to control the COVID-19 pandemic, an important question is how to choose the optimum balance between the full opening of transport networks and the control of epidemic spread. In this work we investigate the interplay between network dismantling and epidemic spread rate as a proxy for the imposition of travel restrictions to control disease spread. For network dismantling we focus on the weighted and unweighted forms of metrics that capture the topological and informational structure of the network. Our results indicate that there is benefit to a directed approach to imposing travel restrictions, but we identify that more detailed models of the transport network are necessary for definitive results.

TPSL: A propagation source localization method based on limited observation nodes

2021 ◽  
Author(s):  
Chao Wang ◽  
Ziqian Man ◽  
Shunjie Yuan ◽  
Gaoyu Zhang
Keyword(s):  
Complex Networks ◽  
Source Localization ◽  
Network Topology ◽  
Probability Distributions ◽  
Closeness Centrality ◽  
Information Propagation ◽  
Excellent Performance ◽  
Localization Method ◽  
Empirical Networks

Abstract The research on localization of propagation sources on complex networks has farreaching significance in various fields. Many source localization methods have been proposed. However, the assumptions of some existing methods are too ideal, which means they cannot be widely deployed on realistic networks. In this paper, we propose a multi-source localization method TPSL based on limited observation nodes and backward diffusion-based algorithm with the consideration of heterogeneity of the propagation probabilities between nodes. Specifically, given a network topology with time and probability distributions, TPSL can infer the sources of propagation by comprehensively considering the time and probability factors in a way that accords with the characteristics of information propagation in reality. The experiments on artificial and empirical networks demonstrate that TPSL has excellent performance on these networks. We also explore the influence of different strategies of choosing observation nodes on TPSL, and find out that choosing the nodes with larger closeness centrality as observation nodes performs better. Moreover, the performance of TPSL does not be affected by the number of sources.

scholarly journals Efficient Localization Algorithm for Near-Field Noncircular Sources via Dual-Polarization Sensor Array

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Jiaqi Song ◽  
Haihong Tao
Keyword(s):  
Source Localization ◽  
Near Field ◽  
Nuisance Parameter ◽  
Estimation Accuracy ◽  
Localization Algorithm ◽  
Dual Polarization ◽  
Rank Reduction ◽  
Localization Accuracy ◽  
Noncircular Signals ◽  
Polarization Sensor

Noncircular signals are widely used in the area of radar, sonar, and wireless communication array systems, which can offer more accurate estimates and detect more sources. In this paper, the noncircular signals are employed to improve source localization accuracy and identifiability. Firstly, an extended real-valued covariance matrix is constructed to transform complex-valued computation into real-valued computation. Based on the property of noncircular signals and symmetric uniform linear array (SULA) which consist of dual-polarization sensors, the array steering vectors can be separated into the source position parameters and the nuisance parameter. Therefore, the rank reduction (RARE) estimators are adopted to estimate the source localization parameters in sequence. By utilizing polarization information of sources and real-valued computation, the maximum number of resolvable sources, estimation accuracy, and resolution can be improved. Numerical simulations demonstrate that the proposed method outperforms the existing methods in both resolution and estimation accuracy.

scholarly journals Community Detection Based on a Preferential Decision Model

Information ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 53
Author(s):  
Jinfang Sheng ◽  
Ben Lu ◽  
Bin Wang ◽  
Jie Hu ◽  
Kai Wang ◽  
...  
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Decision Model ◽  
Structural Characteristics ◽  
Real Life ◽  
Stable State ◽  
Ground Truth ◽  
Label Propagation ◽  
Comparison Algorithms

The research on complex networks is a hot topic in many fields, among which community detection is a complex and meaningful process, which plays an important role in researching the characteristics of complex networks. Community structure is a common feature in the network. Given a graph, the process of uncovering its community structure is called community detection. Many community detection algorithms from different perspectives have been proposed. Achieving stable and accurate community division is still a non-trivial task due to the difficulty of setting specific parameters, high randomness and lack of ground-truth information. In this paper, we explore a new decision-making method through real-life communication and propose a preferential decision model based on dynamic relationships applied to dynamic systems. We apply this model to the label propagation algorithm and present a Community Detection based on Preferential Decision Model, called CDPD. This model intuitively aims to reveal the topological structure and the hierarchical structure between networks. By analyzing the structural characteristics of complex networks and mining the tightness between nodes, the priority of neighbor nodes is chosen to perform the required preferential decision, and finally the information in the system reaches a stable state. In the experiments, through the comparison of eight comparison algorithms, we verified the performance of CDPD in real-world networks and synthetic networks. The results show that CDPD not only has better performance than most recent algorithms on most datasets, but it is also more suitable for many community networks with ambiguous structure, especially sparse networks.

scholarly journals Interplay between $$k$$-core and community structure in complex networks

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Irene Malvestio ◽  
Alessio Cardillo ◽  
Naoki Masuda
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Random Graph ◽  
Shell Structure ◽  
Crucial Role ◽  
Configuration Model ◽  
Graph Models ◽  
Random Graph Models ◽  
Empirical Networks

Abstract The organisation of a network in a maximal set of nodes having at least k neighbours within the set, known as $$k$$ k -core decomposition, has been used for studying various phenomena. It has been shown that nodes in the innermost $$k$$ k -shells play a crucial role in contagion processes, emergence of consensus, and resilience of the system. It is known that the $$k$$ k -core decomposition of many empirical networks cannot be explained by the degree of each node alone, or equivalently, random graph models that preserve the degree of each node (i.e., configuration model). Here we study the $$k$$ k -core decomposition of some empirical networks as well as that of some randomised counterparts, and examine the extent to which the $$k$$ k -shell structure of the networks can be accounted for by the community structure. We find that preserving the community structure in the randomisation process is crucial for generating networks whose $$k$$ k -core decomposition is close to the empirical one. We also highlight the existence, in some networks, of a concentration of the nodes in the innermost $$k$$ k -shells into a small number of communities.

scholarly journals Mean Shift-Based Multisource Localization Method in Wireless Binary Sensor Network

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xiaosheng Yu ◽  
Jianning Chi ◽  
Ying Wang ◽  
Hao Chu
Keyword(s):  
Sensor Network ◽  
Source Localization ◽  
Mean Shift ◽  
High Energy ◽  
False Alarms ◽  
Localization Accuracy ◽  
Pearson Criterion ◽  
Binary Sensor ◽  
Sensing Model ◽  
Novel Method

Source localization is one of the major research contents in the localization research of wireless sensor networks, which has attracted considerable attention for a long period. In recent years, the wireless binary sensor network (WBSN) has been widely used for source localization due to its high energy efficiency. A novel method which is based on WBSN for multiple source localization is presented in this paper. Firstly, the Neyman-Pearson criterion-based sensing model which takes into account the false alarms is utilized to identify the alarmed nodes. Secondly, the mean shift and hierarchical clustering method are performed on the alarmed nodes to obtain the cluster centers as the initial locations of signal sources. Finally, some voting matrices which can improve the localization accuracy are constructed to decide the location of each acoustic source. The simulation results demonstrate that the proposed method can provide a desirable performance superior to some traditional methods in accuracy and efficiency.

scholarly journals Epigrass: a tool to study disease spread in complex networks.

2007 ◽  
Author(s):  
Flávio Codeço Coelho ◽  
Claudia Codeco ◽  
Oswaldo Cruz
Keyword(s):  
Complex Networks ◽  
Disease Spread
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