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Ódor G, Vuckovic J, Ndoye MAS, Thiran P. Source identification via contact tracing in the presence of asymptomatic patients. APPLIED NETWORK SCIENCE 2023; 8:53. [PMID: 37614376 PMCID: PMC10442312 DOI: 10.1007/s41109-023-00566-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 06/26/2023] [Indexed: 08/25/2023]
Abstract
Inferring the source of a diffusion in a large network of agents is a difficult but feasible task, if a few agents act as sensors revealing the time at which they got hit by the diffusion. One of the main limitations of current source identification algorithms is that they assume full knowledge of the contact network, which is rarely the case, especially for epidemics, where the source is called patient zero. Inspired by recent implementations of contact tracing algorithms, we propose a new framework, which we call Source Identification via Contact Tracing Framework (SICTF). In the SICTF, the source identification task starts at the time of the first hospitalization, and initially we have no knowledge about the contact network other than the identity of the first hospitalized agent. We may then explore the network by contact queries, and obtain symptom onset times by test queries in an adaptive way, i.e., both contact and test queries can depend on the outcome of previous queries. We also assume that some of the agents may be asymptomatic, and therefore cannot reveal their symptom onset time. Our goal is to find patient zero with as few contact and test queries as possible. We implement two local search algorithms for the SICTF: the LS algorithm, which has recently been proposed by Waniek et al. in a similar framework, is more data-efficient, but can fail to find the true source if many asymptomatic agents are present, whereas the LS+ algorithm is more robust to asymptomatic agents. By simulations we show that both LS and LS+ outperform previously proposed adaptive and non-adaptive source identification algorithms adapted to the SICTF, even though these baseline algorithms have full access to the contact network. Extending the theory of random exponential trees, we analytically approximate the source identification probability of the LS/ LS+ algorithms, and we show that our analytic results match the simulations. Finally, we benchmark our algorithms on the Data-driven COVID-19 Simulator (DCS) developed by Lorch et al., which is the first time source identification algorithms are tested on such a complex dataset.
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Peng SL, Wang HJ, Peng H, Zhu XB, Li X, Han J, Zhao D, Hu ZL. NLSI: An innovative method to locate epidemic sources on the SEIR propagation model. CHAOS (WOODBURY, N.Y.) 2023; 33:083125. [PMID: 37549113 DOI: 10.1063/5.0152859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 07/12/2023] [Indexed: 08/09/2023]
Abstract
Epidemics pose a significant threat to societal development. Accurately and swiftly identifying the source of an outbreak is crucial for controlling the spread of an epidemic and minimizing its impact. However, existing research on locating epidemic sources often overlooks the fact that epidemics have an incubation period and fails to consider social behaviors like self-isolation during the spread of the epidemic. In this study, we first take into account isolation behavior and introduce the Susceptible-Exposed-Infected-Recovered (SEIR) propagation model to simulate the spread of epidemics. As the epidemic reaches a certain threshold, government agencies or hospitals will report the IDs of some infected individuals and the time when symptoms first appear. The reported individuals, along with their first and second-order neighbors, are then isolated. Using the moment of symptom onset reported by the isolated individuals, we propose a node-level classification method and subsequently develop the node-level-based source identification (NLSI) algorithm. Extensive experiments demonstrate that the NLSI algorithm is capable of solving the source identification problem for single and multiple sources under the SEIR propagation model. We find that the source identification accuracy is higher when the infection rate is lower, and a sparse network structure is beneficial to source localization. Furthermore, we discover that the length of the isolation period has little impact on source localization, while the length of the incubation period significantly affects the accuracy of source localization. This research offers a novel approach for identifying the origin of the epidemic associated with our defined SEIR model.
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Affiliation(s)
- Shui-Lin Peng
- College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China
| | - Hong-Jue Wang
- School of Information, Beijing Wuzi University, Beijing 101149, China
| | - Hao Peng
- College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China
| | - Xiang-Bin Zhu
- College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China
| | - Xiang Li
- College of Science, National University of Defense Technology, Changsha 410073, China
| | - Jianmin Han
- College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China
| | - Dandan Zhao
- College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China
| | - Zhao-Long Hu
- College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China
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Li W, Guo C, Liu Y, Zhou X, Jin Q, Xin M. Rumor source localization in social networks based on infection potential energy. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.03.098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
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4
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Yang F, Li C, Peng Y, Liu J, Yao Y, Wen J, Yang S. Locating the propagation source in complex networks with observers-based similarity measures and direction-induced search. Soft comput 2023; 27:1-27. [PMID: 37362267 PMCID: PMC10072820 DOI: 10.1007/s00500-023-08000-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/24/2023] [Indexed: 04/07/2023]
Abstract
Locating the propagation source is one of the most important strategies to control the harmful diffusion process on complex networks. Most existing methods only consider the infection time information of the observers, but the diffusion direction information of the observers is ignored, which is helpful to locate the source. In this paper, we consider both of the diffusion direction information and the infection time information to locate the source. We introduce a relaxed direction-induced search (DIS) to utilize the diffusion direction information of the observers to approximate the actual diffusion tree on a network. Based on the relaxed DIS, we further utilize the infection time information of the observers to define two kinds of observers-based similarity measures, including the Infection Time Similarity and the Infection Time Order Similarity. With the two kinds of similarity measures and the relaxed DIS, a novel source locating method is proposed. We validate the performance of the proposed method on a series of synthetic and real networks. The experimental results show that the proposed method is feasible and effective in accurately locating the propagation source.
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Affiliation(s)
- Fan Yang
- School of Computer Science and Technology, Guangxi University of Science and Technology, Liuzhou, 545006 China
- Key Laboratory of Intelligent Information Processing and Graph Processing, Guangxi University of Science and Technology, Liuzhou, 545006 China
| | - Chungui Li
- School of Computer Science and Technology, Guangxi University of Science and Technology, Liuzhou, 545006 China
| | - Yong Peng
- School of Computer Science and Technology, Guangxi University of Science and Technology, Liuzhou, 545006 China
| | - Jingxian Liu
- School of Computer Science and Technology, Guangxi University of Science and Technology, Liuzhou, 545006 China
- Key Laboratory of Intelligent Information Processing and Graph Processing, Guangxi University of Science and Technology, Liuzhou, 545006 China
| | - Yabing Yao
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050 China
| | - Jiayan Wen
- School of Computer Science and Technology, Guangxi University of Science and Technology, Liuzhou, 545006 China
| | - Shuhong Yang
- School of Computer Science and Technology, Guangxi University of Science and Technology, Liuzhou, 545006 China
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5
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Yang F, Liu J, Zhang R, Yao Y. Diffusion characteristics classification framework for identification of diffusion source in complex networks. PLoS One 2023; 18:e0285563. [PMID: 37186596 PMCID: PMC10184948 DOI: 10.1371/journal.pone.0285563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 04/26/2023] [Indexed: 05/17/2023] Open
Abstract
The diffusion phenomena taking place in complex networks are usually modelled as diffusion process, such as the diffusion of diseases, rumors and viruses. Identification of diffusion source is crucial for developing strategies to control these harmful diffusion processes. At present, accurately identifying the diffusion source is still an opening challenge. In this paper, we define a kind of diffusion characteristics that is composed of the diffusion direction and time information of observers, and propose a neural networks based diffusion characteristics classification framework (NN-DCCF) to identify the source. The NN-DCCF contains three stages. First, the diffusion characteristics are utilized to construct network snapshot feature. Then, a graph LSTM auto-encoder is proposed to convert the network snapshot feature into low-dimension representation vectors. Further, a source classification neural network is proposed to identify the diffusion source by classifying the representation vectors. With NN-DCCF, the identification of diffusion source is converted into a classification problem. Experiments are performed on a series of synthetic and real networks. The results show that the NN-DCCF is feasible and effective in accurately identifying the diffusion source.
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Affiliation(s)
- Fan Yang
- Key Laboratory of Intelligent Information Processing and Graph Processing, Guangxi University of Science and Technology, Liuzhou, Guangxi, China
- School of Computer Science and Technology, Guangxi University of Science and Technology, Liuzhou, Guangxi, China
| | - Jingxian Liu
- Key Laboratory of Intelligent Information Processing and Graph Processing, Guangxi University of Science and Technology, Liuzhou, Guangxi, China
- School of Computer Science and Technology, Guangxi University of Science and Technology, Liuzhou, Guangxi, China
| | - Ruisheng Zhang
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, China
| | - Yabing Yao
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, Gansu, China
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Waniek M, Holme P, Farrahi K, Emonet R, Cebrian M, Rahwan T. Trading contact tracing efficiency for finding patient zero. Sci Rep 2022; 12:22582. [PMID: 36585429 PMCID: PMC9801158 DOI: 10.1038/s41598-022-26892-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 12/21/2022] [Indexed: 12/31/2022] Open
Abstract
As the COVID-19 pandemic has demonstrated, identifying the origin of a pandemic remains a challenging task. The search for patient zero may benefit from the widely-used and well-established toolkit of contact tracing methods, although this possibility has not been explored to date. We fill this gap by investigating the prospect of performing the source detection task as part of the contact tracing process, i.e., the possibility of tuning the parameters of the process in order to pinpoint the origin of the infection. To this end, we perform simulations on temporal networks using a recent diffusion model that recreates the dynamics of the COVID-19 pandemic. We find that increasing the budget for contact tracing beyond a certain threshold can significantly improve the identification of infected individuals but has diminishing returns in terms of source detection. Moreover, disease variants of higher infectivity make it easier to find the source but harder to identify infected individuals. Finally, we unravel a seemingly-intrinsic trade-off between the use of contact tracing to either identify infected nodes or detect the source of infection. This trade-off suggests that focusing on the identification of patient zero may come at the expense of identifying infected individuals.
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Affiliation(s)
- Marcin Waniek
- grid.440573.10000 0004 1755 5934Computer Science, Science Division, New York University Abu Dhabi, Abu Dhabi, UAE
| | - Petter Holme
- grid.5373.20000000108389418Aalto University, Espoo, Finland ,grid.31432.370000 0001 1092 3077Kobe University, Kobe, Japan
| | - Katayoun Farrahi
- grid.5491.90000 0004 1936 9297University of Southampton, Southampton, UK
| | - Rémi Emonet
- grid.7849.20000 0001 2150 7757CNRS, Laboratoire Hubert Curien UMR 5516, UJM-Saint-Etienne, Université de Lyon, Saint-Étienne, France
| | - Manuel Cebrian
- grid.419526.d0000 0000 9859 7917Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany ,grid.7840.b0000 0001 2168 9183Statistics Department, Universidad Carlos III de Madrid, Madrid, Spain ,grid.7840.b0000 0001 2168 9183UC3M-Santander Big Data Institute, Universidad Carlos III de Madrid, Madrid, Spain
| | - Talal Rahwan
- grid.440573.10000 0004 1755 5934Computer Science, Science Division, New York University Abu Dhabi, Abu Dhabi, UAE
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7
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Social diffusion sources can escape detection. iScience 2022; 25:104956. [PMID: 36093057 PMCID: PMC9459693 DOI: 10.1016/j.isci.2022.104956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 04/25/2022] [Accepted: 08/12/2022] [Indexed: 11/16/2022] Open
Abstract
Influencing others through social networks is fundamental to all human societies. Whether this happens through the diffusion of rumors, opinions, or viruses, identifying the diffusion source (i.e., the person that initiated it) is a problem that has attracted much research interest. Nevertheless, existing literature has ignored the possibility that the source might strategically modify the network structure (by rewiring links or introducing fake nodes) to escape detection. Here, without restricting our analysis to any particular diffusion scenario, we close this gap by evaluating two mechanisms that hide the source—one stemming from the source’s actions, the other from the network structure itself. This reveals that sources can easily escape detection, and that removing links is far more effective than introducing fake nodes. Thus, efforts should focus on exposing concealed ties rather than planted entities; such exposure would drastically improve our chances of detecting the diffusion source. We study the problem of hiding the diffusion source from source detection algorithms Finding an optimal way of hiding the source is usually computationally intractable In many cases, the source is well hidden without any strategic modifications If the source is exposed, simple heuristic solutions allow it to avoid detection
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Lv W, Zhou W, Gao B, Han Y, Fang H. New Insights Into the Social Rumor Characteristics During the COVID-19 Pandemic in China. Front Public Health 2022; 10:864955. [PMID: 35832275 PMCID: PMC9271676 DOI: 10.3389/fpubh.2022.864955] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 05/23/2022] [Indexed: 11/30/2022] Open
Abstract
Background In the early stage of the COVID-19 outbreak in China, several social rumors in the form of false news, conspiracy theories, and magical cures had ever been shared and spread among the general public at an alarming rate, causing public panic and increasing the complexity and difficulty of social management. Therefore, this study aims to reveal the characteristics and the driving factors of the social rumors during the COVID-19 pandemic. Methods Based on a sample of 1,537 rumors collected from Sina Weibo's debunking account, this paper first divided the sample into four categories and calculated the risk level of all kinds of rumors. Then, time evolution analysis and correlation analysis were adopted to study the time evolution characteristics and the spatial and temporal correlation characteristics of the rumors, and the four stages of development were also divided according to the number of rumors. Besides, to extract the key driving factors from 15 rumor-driving factors, the social network analysis method was used to investigate the driver-driver 1-mode network characteristics, the generation driver-rumor 2-mode network characteristics, and the spreading driver-rumor 2-mode characteristics. Results Research findings showed that the number of rumors related to COVID-19 were gradually decreased as the outbreak was brought under control, which proved the importance of epidemic prevention and control to maintain social stability. Combining the number and risk perception levels of the four types of rumors, it could be concluded that the Creating Panic-type rumors were the most harmful to society. The results of rumor drivers indicated that panic psychology and the lag in releasing government information played an essential role in driving the generation and spread of rumors. The public's low scientific literacy and difficulty in discerning highly confusing rumors encouraged them to participate in spreading rumors. Conclusion The study revealed the mechanism of rumors. In addition, studies involving rumors on different emergencies and social platforms are warranted to enrich the findings.
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Affiliation(s)
- Wei Lv
- School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, China
- *Correspondence: Wei Lv
| | - Wennan Zhou
- School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, China
| | - Binli Gao
- Department of Hyperbaric Oxygen Treatment Center, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Binli Gao
| | - Yefan Han
- School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, China
| | - Han Fang
- School of Architecture, Southwest Jiaotong University, Chengdu, China
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Research on Cascading Fault Location of Chemical Material Networks Based on BFS-Time-Reversal Backpropagation Algorithm. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022. [DOI: 10.1007/s13369-022-06967-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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10
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Hiram Guzzi P, Petrizzelli F, Mazza T. Disease spreading modeling and analysis: a survey. Brief Bioinform 2022; 23:6606045. [PMID: 35692095 DOI: 10.1093/bib/bbac230] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 05/16/2022] [Accepted: 05/17/2022] [Indexed: 12/18/2022] Open
Abstract
MOTIVATION The control of the diffusion of diseases is a critical subject of a broad research area, which involves both clinical and political aspects. It makes wide use of computational tools, such as ordinary differential equations, stochastic simulation frameworks and graph theory, and interaction data, from molecular to social granularity levels, to model the ways diseases arise and spread. The coronavirus disease 2019 (COVID-19) is a perfect testbench example to show how these models may help avoid severe lockdown by suggesting, for instance, the best strategies of vaccine prioritization. RESULTS Here, we focus on and discuss some graph-based epidemiological models and show how their use may significantly improve the disease spreading control. We offer some examples related to the recent COVID-19 pandemic and discuss how to generalize them to other diseases.
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Affiliation(s)
- Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, Magna Graecia University, Catanzaro, 88110, Italy
| | - Francesco Petrizzelli
- Bioinformatics unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, 71013, Italy
| | - Tommaso Mazza
- Bioinformatics unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, 71013, Italy
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Petrizzelli F, Guzzi PH, Mazza T. Beyond COVID-19 Pandemic: Topology-aware optimization of vaccination strategy for minimizing virus spreading. Comput Struct Biotechnol J 2022; 20:2664-2671. [PMID: 35664237 PMCID: PMC9135485 DOI: 10.1016/j.csbj.2022.05.040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 05/19/2022] [Accepted: 05/19/2022] [Indexed: 12/12/2022] Open
Abstract
Paper discusses the relevance of the adoption of ad-hoc vaccination strategies. Paper shows how to evaluate the impact of different vaccination strategy by considering network-based models. Tailored interventions, e.g., vaccination, applied on central nodes of these networks may efficiently stop the propagation of an infection. The way node "centrality" is defined is the key to curb infection spreading.
The mitigation of an infectious disease spreading has recently gained considerable attention from the research community. It may be obtained by adopting sanitary measurements (e.g., vaccination, wearing masks), social rules (e.g., social distancing), together with an extensive vaccination campaign. Vaccination is currently the primary way for mitigating the Coronavirus Disease (COVID-19) outbreak without severe lockdown. Its effectiveness also depends on the number and timeliness of administrations and thus demands strict prioritization criteria. Almost all countries have prioritized similar classes of exposed workers: healthcare professionals and the elderly, obtaining to maximize the survival of patients and years of life saved. Nevertheless, the virus is currently spreading at high rates, and any prioritization criterion so far adopted did not account for the structural organization of the contact networks. We reckon that a network where nodes are people while the edges represent their social contacts may efficiently model the virus’s spreading. It is known that tailored interventions (e.g., vaccination) on central nodes may efficiently stop the propagation, thereby eliminating the “bridge edges.” We then introduce such a model and consider both synthetic and real datasets. We present the benefits of a topology-aware versus an age-based vaccination strategy to mitigate the spreading of the virus. The code is available at https://github.com/mazzalab/playgrounds.
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Affiliation(s)
- Francesco Petrizzelli
- Laboratory of Bioinformatics, Fondazione IRCCS Casa Sollievo della Sofferenza, Viale Capuccini, 71013 S. Giovanni Rotondo, Fg, Italy
| | - Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, University of Catanzaro, Catanzaro, Campus S Venuta, 88100, Italy
- Corresponding authors.
| | - Tommaso Mazza
- Laboratory of Bioinformatics, Fondazione IRCCS Casa Sollievo della Sofferenza, Viale Capuccini, 71013 S. Giovanni Rotondo, Fg, Italy
- Corresponding authors.
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12
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Comparison of observer based methods for source localisation in complex networks. Sci Rep 2022; 12:5079. [PMID: 35332184 PMCID: PMC8948209 DOI: 10.1038/s41598-022-09031-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 03/09/2022] [Indexed: 11/09/2022] Open
Abstract
In recent years, research on methods for locating a source of spreading phenomena in complex networks has seen numerous advances. Such methods can be applied not only to searching for the "patient zero" in epidemics, but also finding the true sources of false or malicious messages circulating in the online social networks. Many methods for solving this problem have been established and tested in various circumstances. Yet, we still lack reviews that would include a direct comparison of efficiency of these methods. In this paper, we provide a thorough comparison of several observer-based methods for source localisation on complex networks. All methods use information about the exact time of spread arrival at a pre-selected group of vertices called observers. We investigate how the precision of the studied methods depends on the network topology, density of observers, infection rate, and observers' placement strategy. The direct comparison between methods allows for an informed choice of the methods for applications or further research. We find that the Pearson correlation based method and the method based on the analysis of multiple paths are the most effective in networks with synthetic or real topologies. The former method dominates when the infection rate is low; otherwise, the latter method takes over.
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Suthanthiradevi P, Karthika S. Veracity assessment by single and multi-source identification algorithms during the crisis. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-210540] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Social networks have become a popular communication tool for information sharing. Twitter offers access to data and provides a significant opportunity to analyze data. During pandemics, Twitter becomes a big source for the dispersal of unverified information. In social media, it is difficult to find the sources of rumors. To tackle this problem the authors have developed a hybrid rumor centrality algorithm for rumor source detection in social networks. The authors propose an S-RSI algorithm for identifying a single rumor centre and an M-RSI algorithm for identifying the propagations of multiple rumor centres in the thread of conversation. The proposed rumor centrality algorithm efficiently predicts the rumor disseminating possibilities in a conversation tree with the aid of graph theoretical approach. The authors have evaluated the performance of the algorithms on the PHEME dataset containing seven real-time event conversational trees based on the tweet messages. The results show that the proposed is best suitable in finding the rumor source centre with a high probability in social media during a crisis.
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Affiliation(s)
- P. Suthanthiradevi
- Department of Information Technology, Sri Sivasubramaniya Nadar College of Engineering, Chennai, Tamil Nadu, India
| | - S. Karthika
- Department of Information Technology, Sri Sivasubramaniya Nadar College of Engineering, Chennai, Tamil Nadu, India
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14
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Gajewski ŁG, Sienkiewicz J, Hołyst JA. Discovering hidden layers in quantum graphs. Phys Rev E 2021; 104:034311. [PMID: 34654079 DOI: 10.1103/physreve.104.034311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 09/09/2021] [Indexed: 11/07/2022]
Abstract
Finding hidden layers in complex networks is an important and a nontrivial problem in modern science. We explore the framework of quantum graphs to determine whether concealed parts of a multilayer system exist and if so then what is their extent, i.e., how many unknown layers are there. Assuming that the only information available is the time evolution of a wave propagation on a single layer of a network it is indeed possible to uncover that which is hidden by merely observing the dynamics. We present evidence on both synthetic and real-world networks that the frequency spectrum of the wave dynamics can express distinct features in the form of additional frequency peaks. These peaks exhibit dependence on the number of layers taking part in the propagation and thus allowing for the extraction of said number. We show that, in fact, with sufficient observation time, one can fully reconstruct the row-normalized adjacency matrix spectrum. We compare our propositions to a machine learning approach using a wave packet signature method modified for the purposes of multilayer systems.
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Affiliation(s)
- Łukasz G Gajewski
- Faculty of Physics, Warsaw University of Technology, Koszykowa 75, 00-662 Warszawa, Poland
| | - Julian Sienkiewicz
- Faculty of Physics, Warsaw University of Technology, Koszykowa 75, 00-662 Warszawa, Poland
| | - Janusz A Hołyst
- Faculty of Physics, Warsaw University of Technology, Koszykowa 75, 00-662 Warszawa, Poland and ITMO University, Kronverkskiy Prospekt 49, St Petersburg, Russia 197101
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15
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Schlosser F, Brockmann D. Finding disease outbreak locations from human mobility data. EPJ DATA SCIENCE 2021; 10:52. [PMID: 34692370 PMCID: PMC8525067 DOI: 10.1140/epjds/s13688-021-00306-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 10/05/2021] [Indexed: 05/09/2023]
Abstract
UNLABELLED Finding the origin location of an infectious disease outbreak quickly is crucial in mitigating its further dissemination. Current methods to identify outbreak locations early on rely on interviewing affected individuals and correlating their movements, which is a manual, time-consuming, and error-prone process. Other methods such as contact tracing, genomic sequencing or theoretical models of epidemic spread offer help, but they are not applicable at the onset of an outbreak as they require highly processed information or established transmission chains. Digital data sources such as mobile phones offer new ways to find outbreak sources in an automated way. Here, we propose a novel method to determine outbreak origins from geolocated movement data of individuals affected by the outbreak. Our algorithm scans movement trajectories for shared locations and identifies the outbreak origin as the most dominant among them. We test the method using various empirical and synthetic datasets, and demonstrate that it is able to single out the true outbreak location with high accuracy, requiring only data of N = 4 individuals. The method can be applied to scenarios with multiple outbreak locations, and is even able to estimate the number of outbreak sources if unknown, while being robust to noise. Our method is the first to offer a reliable, accurate out-of-the-box approach to identify outbreak locations in the initial phase of an outbreak. It can be easily and quickly applied in a crisis situation, improving on previous manual approaches. The method is not only applicable in the context of disease outbreaks, but can be used to find shared locations in movement data in other contexts as well. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1140/epjds/s13688-021-00306-6.
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Affiliation(s)
- Frank Schlosser
- Department of Physics, Humboldt-University of Berlin, Newtonstr. 15, 12489 Berlin, Germany
- Complex Systems Group, Robert Koch-Institute, Nordufer 20, 13353 Berlin, Germany
| | - Dirk Brockmann
- Institute for Theoretical Biology, Humboldt-University of Berlin, Philippstr. 13, 10115 Berlin, Germany
- Complex Systems Group, Robert Koch-Institute, Nordufer 20, 13353 Berlin, Germany
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16
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Locating the propagation source in complex networks with a direction-induced search based Gaussian estimator. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.105674] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Hu ZL, Wang L, Tang CB. Locating the source node of diffusion process in cyber-physical networks via minimum observers. CHAOS (WOODBURY, N.Y.) 2019; 29:063117. [PMID: 31266325 DOI: 10.1063/1.5092772] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 05/31/2019] [Indexed: 06/09/2023]
Abstract
Locating the source node that initiates a diffusion process is an increasingly popular topic that contributes new insights into the maintenance of cyber security, rumor detection in social media, digital surveillance of infectious diseases, etc. Existing studies select the observers randomly or select them heuristically according to the network centrality or community measures. However, there still lacks a method to identify the minimum set of observers for accurately locating the source node of information diffusion in cyber physical networks. Here, we fill this knowledge gap by proposing a greedy optimization algorithm by analyzing the differences of the propagation delay. We use extensive simulations with both synthetic and empirical networks to show that the number of observers can be substantially decreased: Our method only uses a small fraction of nodes (10%-20%) as observers in most networks, whereas the conventional random selection methods have to use 2-3 times more nodes as observers. Interestingly, if a network has a large proportion of low-degree nodes (e.g., karate network), it is necessary to recruit more observers. In particular, the periphery nodes that are only connected with one edge must be observers. Combining our greedy optimization algorithm with the diffusion-back method, the performance of source localization is robust against noise.
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Affiliation(s)
- Z L Hu
- College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004, People's Republic of China
| | - L Wang
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, 75015 Paris, France
| | - C B Tang
- College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, People's Republic of China
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