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2720. Characterisation of robustness measures for complex networks under attacks
Invited abstract in session TB-6: Advancements of OR-Analytics in Statistics, Machine Learning and Data Science 13, stream Advancements of OR-analytics in statistics, machine learning and data science.
Tuesday, 10:30-12:00Room: 1013 (building: 202)
Authors (first author is the speaker)
| 1. | Reetendra Singh
|
| Decision Sciences, Indian Institute of Management Visakhapatnam | |
| 2. | shivshanker singh patel
|
| Decision Sciences, Indian Institute of management visakhapatnam |
Abstract
In the globe, human and non-human systems are highly connected as complex networks. Complexity is understood as the property that is defined by the nature of interconnectedness, links and size of the network. As example, business and society are made of supply chain and societal community network. These networks are susceptible to attacks; we have considered two scenarios: (A) purposeful attack and (B) random attack. This study aims to investigate the robustness of complex networks during the attacks and how the complexity of networks changes under the attacks. And suggests algorithms and analytics to identify vulnerable complex networks. The application is shown on two different data sets for complex networks. The first is for the supply chain with materials flow and contractual relationships networks. The second is for community networks. Results and analysis show that some complex networks are less vulnerable to attacks, and some are more vulnerable. Further, the results show the characterisation of the robustness measure for different complex networks during attacks and the changes in the network property under the attacks.
Keywords
- Complex Societal Problems
- Network Design
- Algorithms
Status: accepted
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