Influencers identification in complex networks through reaction-diffusion dynamics
arXiv:1803.01212 · doi:10.1103/PhysRevE.98.062302
Abstract
A pivotal idea in network science, marketing research and innovation diffusion theories is that a small group of nodes -- called influencers -- have the largest impact on social contagion and epidemic processes in networks. Despite the long-standing interest in the influencers identification problem in socio-economic and biological networks, there is not yet agreement on which is the best identification strategy. State-of-the-art strategies are typically based either on heuristic centrality metrics or on analytic arguments that only hold for specific network topologies or peculiar dynamical regimes. Here, we leverage the recently introduced random-walk effective distance -- a topological metric that estimates almost perfectly the arrival time of diffusive spreading processes on networks -- to introduce a new centrality metric which quantifies how close a node is to the other nodes. We show that the new centrality metric significantly outperforms state-of-the-art metrics in detecting the influencers for global contagion processes. Our findings reveal the essential role of the network effective distance for the influencers identification and lead us closer to the optimal solution of the problem.
References in corpus (30)
- Epidemic spreading in scale-free networks
- Finding community structure in networks using the eigenvectors of matrices
- Epidemic processes in complex networks
- Identification of influential spreaders in complex networks
- The Dynamics of Viral Marketing
- Influence maximization in complex networks through optimal percolation
- Prediction and predictability of global epidemics: the role of the airline transportation network
- Efficient Immunization Strategies for Computer Networks and Populations
- Vital nodes identification in complex networks
- Community Structure in Jazz
- Reaction-diffusion processes and metapopulation models in heterogeneous networks
- Spectral redemption: clustering sparse networks
- Ranking spreaders by decomposing complex networks
- Memory in network flows and its effects on spreading dynamics and community detection
- Searching for superspreaders of information in real-world social media
- Localization and centrality in networks
- Causality-Driven Slow-Down and Speed-Up of Diffusion in Non-Markovian Temporal Networks
- Ranking the spreading influence in complex networks
- Absence of influential spreaders in rumor dynamics
- Ranking in evolving complex networks
- Predictability and epidemic pathways in global outbreaks of infectious diseases: the SARS case study
- Understanding the spreading power of all nodes in a network: a continuous-time perspective
- Global mean first-passage times of random walks on complex networks
- Effective Distances for Epidemics Spreading on Complex Networks
- Identifying influential spreaders and efficiently estimating infection numbers in epidemic models: a walk counting approach
- Ranking and clustering of nodes in networks with smart teleportation
- Temporal network structures controlling disease spreading
- Leveraging percolation theory to single out influential spreaders in networks
- Topological structure and the H-index in complex networks
- Ranking influential spreaders is an ill-defined problem