Fragmenting networks by targeting collective influencers at a mesoscopic level

  title={Fragmenting networks by targeting collective influencers at a mesoscopic level},
  author={Teruyoshi Kobayashi and Naoki Masuda},
  journal={Scientific Reports},
A practical approach to protecting networks against epidemic processes such as spreading of infectious diseases, malware, and harmful viral information is to remove some influential nodes beforehand to fragment the network into small components. Because determining the optimal order to remove nodes is a computationally hard problem, various approximate algorithms have been proposed to efficiently fragment networks by sequential node removal. Morone and Makse proposed an algorithm employing the… 
6 Citations
Performance of attack strategies on modular networks
This work deeply explore the trade-off associated with attack procedures, introducing a generalized robustness measure and presenting an attack performance index that takes into account both robustness of the network against the attack and the run-time needed to obtained the list of targeted nodes for the attack.
Influencer identification in dynamical complex systems
This review surveys recent advances in the study of influencer identification developed from different perspectives, and presents state-of-the-art solutions designed for different objectives.
NUP155 insufficiency recalibrates a pluripotent transcriptome with network remodeling of a cardiogenic signaling module
Here, NUP155 regulates cardioplasticity of a sub-network embedded within a larger framework of genome integrity, and exemplifies how transcriptome cardiogenicity in an embryonic stem cell genome is recalibrated by nucleoporin dysfunction.


A new immunization algorithm based on spectral properties for complex networks
  • R. Zahedi, M. Khansari
  • Computer Science
    2015 7th Conference on Information and Knowledge Technology (IKT)
  • 2015
A new algorithm is proposed that minimizes the worst expected growth of an epidemic by reducing the size of the largest connected component of the underlying contact network by using Laplacian spectral partitioning.
An Efficient Immunization Strategy for Community Networks
This work proposes and study an effective algorithm that searches for bridge hubs, which are bridge nodes with a larger number of weak ties, as immunizing targets based on the idea of referencing to an expanding friendship circle as a self-avoiding walk proceeds, and shows that the algorithm is more effective than other existing local algorithms for a given immunization coverage.
Fast Fragmentation of Networks Using Module-Based Attacks
It is shown that the module-based approach always outperforms targeted attacks to vertices based on node degree or betweenness centrality rankings, with gains in efficiency strongly related to the modularity of the network.
Global efficiency of local immunization on complex networks
This study uses different characteristics of network organization to identify the influential spreaders in 17 empirical networks of diverse nature using 2 epidemic models and develops an analytical framework that highlights a transition in the characteristic scale of different epidemic regimes.
Influence maximization in complex networks through optimal percolation
This work maps the problem onto optimal percolation in random networks to identify the minimal set of influencers, which arises by minimizing the energy of a many-body system, where the form of the interactions is fixed by the non-backtracking matrix of the network.
Immunization of networks with community structure
An analytical framework for immunizing modular networks is developed on the basis of an immunization strategy based on eigenvector centrality and the contribution of each node to the connectivity in a coarse-grained network among modules is quantified.
Community detection algorithms: a comparative analysis.
Three recent algorithms introduced by Rosvall and Bergstrom and Ronhovde and Nussinov have an excellent performance, with the additional advantage of low computational complexity, which enables one to analyze large systems.
Collective Influence Algorithm to find influencers via optimal percolation in massively large social media
Two Belief-Propagation variants of CI that consider global optimization via message-passing are introduced that identify a slightly smaller fraction of influencers than CI and, remarkably, reproduce the exact analytical optimal percolation threshold obtained in Random Struct.
Near linear time algorithm to detect community structures in large-scale networks.
This paper investigates a simple label propagation algorithm that uses the network structure alone as its guide and requires neither optimization of a predefined objective function nor prior information about the communities.
Dynamics and Control of Diseases in Networks with Community Structure
It is found that community structure has a major impact on disease dynamics, and it is shown that in networks with strong community structure, immunization interventions targeted at individuals bridging communities are more effective than those simply targeting highly connected individuals.