Identifying influential spreaders in complex networks based on gravity formula
arXiv:1505.02476 · doi:10.1016/j.physa.2015.12.162
Abstract
How to identify the influential spreaders in social networks is crucial for accelerating/hindering information diffusion, increasing product exposure, controlling diseases and rumors, and so on. In this paper, by viewing the k-shell value of each node as its mass and the shortest path distance between two nodes as their distance, then inspired by the idea of the gravity formula, we propose a gravity centrality index to identify the influential spreaders in complex networks. The comparison between the gravity centrality index and some well-known centralities, such as degree centrality, betweenness centrality, closeness centrality, and k-shell centrality, and so forth, indicates that our method can effectively identify the influential spreaders in real networks as well as synthetic networks. We also use the classical Susceptible-Infected-Recovered (SIR) epidemic model to verify the good performance of our method.
4 tables and 4 figures, accepted by Physica A
References in corpus (12)
- Finding community structure in networks using the eigenvectors of matrices
- Predicting Missing Links via Local Information
- Leaders in Social Networks, the Delicious Case
- Ranking spreaders by decomposing complex networks
- Searching for superspreaders of information in real-world social media
- Graph Evolution: Densification and Shrinking Diameters
- Avoiding catastrophic failure in correlated networks of networks
- A k-shell decomposition method for weighted networks
- Ranking the spreading influence in complex networks
- Identifying effective multiple spreaders by coloring complex networks
- Iterative resource allocation based on propagation feature of node for identifying the influential nodes
- Locating influential nodes via dynamics-sensitive centrality
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- Combined Centrality Measures for an Improved Characterization of Influence Spread in Social Networks
- EMH: Extended Mixing H-index centrality for identification important users in social networks based on neighborhood diversity
- Mining Influential Spreaders in Complex Networks by an Effective Combination of the Degree and K-Shell
- Finding the proper node ranking method for complex networks
- Estimating the Expected Influence Capacities of Nodes in Complex Networks under the Susceptible-Infectious-Recovered (SIR) Model
- Maximizing spreading influence via measuring influence overlap for social networks
- Gravity models of networks: integrating maximum-entropy and econometric approaches
- Ranking the spreading influence of nodes in complex networks based on mixing degree centrality and local structure
- Measuring the influence of beliefs in belief networks
- A novel method based on node correlation to evaluate the important nodes in complex networks