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 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 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.

Modern Diffusion of Products with Complex Network Models

2011 ◽  
pp. 119-133
Author(s):  
Atsushi Tanaka
Keyword(s):  
Complex Networks ◽  
Marketing Strategy ◽  
Complex Network ◽  
Network Models ◽  
Information Propagation ◽  
Multiagent Simulation ◽  
Product Marketing ◽  
Winner Takes All ◽  
Complex Network Models

In this chapter, some important matters of complex networks and their models are reviewed shortly, and then the modern diffusion of products under the information propagation using multiagent simulation is discussed. The remarkable phenomena like “Winner-Takes-All” and “Chasm” can be observed, and one product marketing strategy is also proposed.

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