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Unlike classical centrality measures, recently developed community-aware centrality measures use a network’s community structure to identify influential nodes in complex networks. This paper investigates their relationship on a set of fifty real-world networks originating from various domains. Results show that classical and community-aware centrality measures generally exhibit low to medium correlation values. These results are consistent across networks. Transitivity and efficiency are the most influential macroscopic network features driving the correlation variation between classical and community-aware centrality measures. Additionally, the mixing parameter, the modularity, and the Max-ODF are the main mesoscopic topological properties exerting the most substantial effect.

International audience; Identifying influential nodes in social networks is a fundamental issue. Indeed, it has many applications, such as inhibiting epidemic spreading, accelerating information diffusion, preventing terrorist attacks, and much more. Classically, centrality measures quantify the node's importance based on various topological properties of the network, such as Degree and Betweenness. Nonetheless, these measures are agnostic of the community structure, although it is a ubiquitous characteristic encountered in many real-world networks. To overcome this drawback, there is a growing trend to design so-called community-aware centrality measures. Although several works investigate the relationship between various classical centrality measures [1-3], the interplay between classical and community-aware centrality measures is still unexplored. This work presents an extensive investigation aimed at a better understanding of the relationship between community-aware centrality measures, classical centrality measures, and network topology. Artificial and real-world networks are used in the experiments. The Kendall's Tau correlation quantifies the interaction between ten classical and twenty-eight community-aware centrality measures. The community-aware centrality measures are divided into three groups. The first group's ten measures are based on the intra-community links of a node (local measures). The second group's twelve measures are based on the inter-community links of a node (global measures). Finally, the six measures of the third group consider both types of links (mixed measures). The LFR algorithm generates artificial networks with controlled properties. Indeed, the community structure strength (µ), the exponent of the degree distribution (γ), and the community size distribution (θ) can be specified. The experiments show that the community structure strength is the main feature governing the correlation between classical and community-aware centrality measures. The heatmap on the left of Figure 1 represents the correlation in an artificial network with a strong community structure. The global community-aware centrality measures exhibit a low correlation with classical centrality measures, while local communityaware centrality measures show a high correlation. The results are inverted when the network has a weak community structure. Differences are more subtle for mixed community-aware measures. One can also notice that results are relatively insensitive to variation of the degree and community size distributions' exponents. Fifty real-world networks originating from various domains are also investigated. Linear regression is performed considering six macroscopic (

International audience

Community structure is a ubiquitous feature in natural and artificial systems. Identifying key nodes is a fundamental task to speed up or mitigate any diffusive processes in these systems. Centrality measures aim to do so by selecting a small set of critical nodes. Classical centrality measures are agnostic to community structure, while community-aware centrality measures exploit this property. Several works study the relationship between classical centrality measures, but the relationship between classical and community-aware centrality measures is almost unexplored. In this work [1], we answer two questions: (1) How do classical and community-aware centrality measures relate? (2) What is the influence of the network topology on their relationship? We perform an analysis involving a set of popular classical and community-aware centrality measures on synthetic and real-world networks. Communityaware centrality measures can be classified into two groups. The local ones are relying mainly on the intra-community links. The global ones exploit the connections bridging the communities. First, we calculate the Kendall's Tau correlation between all pair of classical and community-aware centrality measures on a set of synthetic LFR networks with controllable parameters (community structure strength (), degree distribution exponent (), and community size distribution exponent ()). Results show that the main feature driving the correlation variation between classical and community-aware centrality measures is the community structure strength. Furthermore, local community-aware centrality measures correlate with classical ones in networks with a strong community structure, while correlation is weak for global community-aware centrality measures. One observes the opposite behavior in networks with a weak community structure. Second, we calculate the Kendall's Tau correlation using a set of 50 real-world networks from various domains. We perform a linear regression analysis to assess the relationship between the networks' macroscopic and mesoscopic features and the mean correlation value. Results show that the community structure strength and transitivity are the most significant features driving the correlation between classical and community-aware centrality measures. These findings open a new perspective on designing community-aware centrality measures tailored to the network topology.

International audience

Centrality measures are crucial in quantifying the influence of the members of a social network. Although there has been a great deal of work dealing with this issue, the vast majority of classical centrality measures are agnostic of the community structure characterizing many social networks. Recent works have developed community-aware centrality measures that exploit features of the community structure information encountered in most real-world complex networks. In this paper, we investigate the interactions between 5 popular classical centrality measures and 5 community-aware centrality measures using 8 real-world online networks. Correlation as well as similarity measures between both types of centrality measures are computed. Results show that community-aware centrality measures can be divided into two groups. The first group, which includes Bridging centrality, Community Hub-Bridge, and Participation Coefficient, provides distinctive node information as compared to classical centrality. This behavior is consistent across the networks. The second group which includes Community-based Mediator and Number of Neighboring Communities is characterized by more mixed results that vary across networks.

International audience

International audience

International audience