Immunization of networks with community structure

Abstract

In this study, an efficient method to immunize modular networks (i.e., networks with community structure) is proposed. The immunization of networks aims at fragmenting networks into small parts with a small number of removed nodes. Its applications include prevention of epidemic spreading, intentional attacks on networks, and conservation of ecosystems. Although preferential immunization of hubs is efficient, good immunization strategies for modular networks have not been established. On the basis of an immunization strategy based on the eigenvector centrality, we develop an analytical framework for immunizing modular networks. To this end, we quantify the contribution of each node to the connectivity in a coarse-grained network among modules. We verify the effectiveness of the proposed method by applying it to model and real networks with modular structure.

3 figures, 1 table

References in corpus (9)

• Community detection in graphs
• Maps of random walks on complex networks reveal community structure
• Efficient Immunization Strategies for Computer Networks and Populations
• Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions
• Characterizing the dynamical importance of network nodes and links
• Efficient local strategies for vaccination and network attack
• Evolutionary dynamics and fixation probabilities in directed networks
• Weighted percolation on directed networks
• Impact of hierarchical modular structure on ranking of individual nodes in directed networks

Cited by in corpus (25)

• Statistical physics of vaccination
• Social physics
• On community structure in complex networks: challenges and opportunities
• Containing epidemic outbreaks by message-passing techniques
• Global efficiency of local immunization on complex networks
• Centrality Measures for Networks with Community Structure
• Measuring Node Contribution to Community Structure with Modularity Vitality
• Opinion control in complex networks
• Identifying an influential spreader from a single seed in complex networks via a message-passing approach
• Effects of diffusion rates on epidemic spreads in metapopulation networks
• Enhancing the spectral gap of networks by node removal
• Effective epidemic containment strategy in hypergraphs
• Optimal Allocation of Resources for Suppressing Epidemic Spreading on Networks
• Immunization Strategies in Networks with Missing Data
• Map Equation Centrality: Community-aware Centrality based on the Map Equation
• Suppressing epidemics on networks by exploiting observer nodes
• Optimal test-kit based intervention strategy of epidemic spreading in heterogeneous complex networks
• Finding influential spreaders from human activity beyond network location
• Consensus ranking for multi-objective interventions in multiplex networks
• Growing networks of overlapping communities with internal structure
• Epidemic spreading in group-structured populations
• Fragmenting networks by targeting collective influencers at a mesoscopic level
• Robustness of correlated networks against propagating attacks
• Comparing the reliability of networks by spectral analysis
• Abrupt transition of the efficient vaccination strategy in a population with heterogeneous fatality rates