Abstract
The robustness is one of the primary characteristics of a real system, which impacts the function and performance of the system. Many real systems in our real world can be formulated as complex networks. It is a feasible method to estimate the robustness of real systems from the perspective of complex networks. The robustness evaluation is one of the basic and hot research topics in the field of complex networks. This paper presents a network-based simulation platform for analyzing and evaluating the robustness of a real system in terms of existing famous measurements. Furthermore, some experiments are implemented in networks with various topologies and scales under the conditions of different types of attacks. The results show that the structural topology is the major factor in the robustness of a network. And malicious attacks result in more damages than random attacks. And there is a correlation among different attack patterns based on various vertex centralities.
Original language | English |
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Title of host publication | Proceedings of 2017 IEEE 6th Data Driven Control and Learning Systems Conference (DDCLS'17) |
Editors | Mingxuan Sun, Huijun Gao |
Place of Publication | Piscataway, NJ |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 638-643 |
Number of pages | 6 |
ISBN (Electronic) | 9781509054619, 9781509054596 |
ISBN (Print) | 9781509054626 |
DOIs | |
Publication status | Published - 2017 |
Externally published | Yes |
Event | 6th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2017 - Chongqing, China Duration: 26 May 2017 → 27 May 2017 |
Conference
Conference | 6th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2017 |
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Country/Territory | China |
City | Chongqing |
Period | 26/05/17 → 27/05/17 |
Keywords
- Complex Network
- Robustness
- Propagation
- Centrality
- Node Ranking