How Overlapping Community Structure Affects Epidemic Spreading in Complex Networks
Many real-world networks exhibit overlapping community structure in which vertices may belong to more than one community. It has been recently shown that community structure plays an import role in epidemic spreading. However, the effect of different vertices on epidemic behavior was still unclear....
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          | Published in | 2014 IEEE 38th International Computer Software and Applications Conference Workshops pp. 240 - 245 | 
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| Main Authors | , , , | 
| Format | Conference Proceeding | 
| Language | English | 
| Published | 
            IEEE
    
        01.07.2014
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| Subjects | |
| Online Access | Get full text | 
| DOI | 10.1109/COMPSACW.2014.43 | 
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| Abstract | Many real-world networks exhibit overlapping community structure in which vertices may belong to more than one community. It has been recently shown that community structure plays an import role in epidemic spreading. However, the effect of different vertices on epidemic behavior was still unclear. In this paper, we classify vertices into overlapping and non-overlapping ones, and investigate in detail how they affect epidemic spreading respectively. We propose a SIR epidemic model named ICP-SIR (Inner-Community Preferred Susceptible-Infective-Recovered) where the inner-community and inter-community spreading rates are different. We consider the case where epidemic process is started by immunizing and infecting multiple overlapping or non-overlapping vertices. The epidemic model is applied on both synthetic and real-world networks. Simulation results indicate that compared to non-overlapping vertices, overlapping vertices play a vital role in spreading the epidemic across communities. The result of our research may provide some reference on epidemic immunization in the future. | 
    
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| AbstractList | Many real-world networks exhibit overlapping community structure in which vertices may belong to more than one community. It has been recently shown that community structure plays an import role in epidemic spreading. However, the effect of different vertices on epidemic behavior was still unclear. In this paper, we classify vertices into overlapping and non-overlapping ones, and investigate in detail how they affect epidemic spreading respectively. We propose a SIR epidemic model named ICP-SIR (Inner-Community Preferred Susceptible-Infective-Recovered) where the inner-community and inter-community spreading rates are different. We consider the case where epidemic process is started by immunizing and infecting multiple overlapping or non-overlapping vertices. The epidemic model is applied on both synthetic and real-world networks. Simulation results indicate that compared to non-overlapping vertices, overlapping vertices play a vital role in spreading the epidemic across communities. The result of our research may provide some reference on epidemic immunization in the future. | 
    
| Author | Cheng Wu Lianchen Liu Jiaxing Shang Feng Xie  | 
    
| Author_xml | – sequence: 1 surname: Jiaxing Shang fullname: Jiaxing Shang email: shangjx06@mails.tsinghua.edu.cn organization: Nat. CIMS Eng. Center, Tsinghua Univ., Beijing, China – sequence: 2 surname: Lianchen Liu fullname: Lianchen Liu email: liulianchen@tsinghua.edu.cn organization: Nat. CIMS Eng. Center, Tsinghua Univ., Beijing, China – sequence: 3 surname: Feng Xie fullname: Feng Xie email: xief10@mails.tsinghua.edu.cn organization: Dept. of Autom., Tsinghua Univ., Beijing, China – sequence: 4 surname: Cheng Wu fullname: Cheng Wu email: wuc@tsinghua.edu.cn organization: Nat. CIMS Eng. Center, Tsinghua Univ., Beijing, China  | 
    
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| Snippet | Many real-world networks exhibit overlapping community structure in which vertices may belong to more than one community. It has been recently shown that... | 
    
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| SubjectTerms | Analytical models Benchmark testing Blogs Communities Community Structure Complex networks Conferences Electronic mail Epidemic Spreading Overlapping  | 
    
| Title | How Overlapping Community Structure Affects Epidemic Spreading in Complex Networks | 
    
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