The role of centrality for the identification of influential spreaders in complex networks (1404.4528v1)

Abstract: The identification of the most influential spreaders in networks is important to control and understand the spreading capabilities of the system as well as to ensure an efficient information diffusion such as in rumor-like dynamics. Recent works have suggested that the identification of influential spreaders is not independent of the dynamics being studied. For instance, the key disease spreaders might not necessarily be so when it comes to analyze social contagion or rumor propagation. Additionally, it has been shown that different metrics (degree, coreness, etc) might identify different influential nodes even for the same dynamical processes with diverse degree of accuracy. In this paper, we investigate how nine centrality measures correlate with the disease and rumor spreading capabilities of the nodes that made up different synthetic and real-world (both spatial and non-spatial) networks. We also propose a generalization of the random walk accessibility as a new centrality measure and derive analytical expressions for the latter measure for simple network configurations. Our results show that for non-spatial networks, the $k$-core and degree centralities are most correlated to epidemic spreading, whereas the average neighborhood degree, the closeness centrality and accessibility are most related to rumor dynamics. On the contrary, for spatial networks, the accessibility measure outperforms the rest of centrality metrics in almost all cases regardless of the kind of dynamics considered. Therefore, an important consequence of our analysis is that previous studies performed in synthetic random networks cannot be generalized to the case of spatial networks.

The Role of Centrality in Identifying Influential Spreaders in Complex Networks

In this paper, the authors explore the significance of centrality measures in identifying influential spreaders in complex networks. The primary focus is on evaluating how certain centrality metrics are correlated with the dissemination potential of nodes in both epidemic and rumor-like spreading processes.

Centrality Measures and Spreading Processes

A total of nine centrality measures were scrutinized, including degree, k-core, average neighborhood degree, closeness, betweenness, eigenvector, clustering coefficient, PageRank, and a new generalized centrality called accessibility. The relationship between these metrics and spreading potential was analyzed in the context of two distinct types of dynamical systems: disease spreading, modeled through the Susceptible-Infectious-Recovered (SIR) framework, and rumor propagation.

Study on Spatial and Non-Spatial Networks

The paper encompasses both spatially embedded networks, like road maps of countries, and non-spatial networks, including social networks and synthetic models such as Barabási-Albert, Waxman, and spatial scale-free networks. The findings highlight a stark contrast in the relevance of centrality metrics across these network types. In non-spatial networks, k-core and degree centralities showed high correlation with the impact of disease spreading, consistent with prior literature. However, these centralities were not as effective in the context of rumor dynamics, where distance-based metrics like closeness centrality, along with new metrics like average neighborhood degree, provided better estimates of influence.

In spatial networks, the paper emphasizes the generalized accessibility centrality, which consistently demonstrated the highest correlation with spreading capabilities. This new metric is particularly adept at capturing the influence of a node based on a weighted accumulation of various walk lengths in the network, emphasizing its access diversity. The analytical appeal of generalized accessibility lies in its ability to incorporate paths of all lengths between node pairs, effectively penalizing longer paths and fostering an understanding of effective spreading potential.

Implications and Future Directions

The results from this work suggest that the choice of centrality measure significantly influences the identification of influential spreaders and is contingent upon both network topology and dynamics. This indicates that conclusions drawn from studies focusing exclusively on non-spatial or purely synthetic networks do not generalize well to real-world, spatially-embedded networks.

The introduction and successful application of the generalized accessibility centrality measure open avenues for further research across various types of networks and spreading processes. Future work could investigate its applicability to other forms of dynamics, such as those found in social or synchronized networks, and validate the efficacy of different diversity-based centrality formulations.

This comprehensive analysis underlines the necessity for multilateral approaches when dealing with real-world networks in the quest to accurately identify nodes with a high propensity to disseminate information or diseases, potentially guiding optimized strategies for system interventions across diverse domains.