Item request has been placed! ×
Item request cannot be made. ×
loading  Processing Request

Analyzing the Correlation of Classical and Community-aware Centrality Measures in Complex Networks

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
  • معلومة اضافية
    • Contributors:
      Université de Bourgogne (UB); Laboratoire d'Informatique de Bourgogne Dijon (LIB)
    • بيانات النشر:
      HAL CCSD
    • الموضوع:
      2021
    • Collection:
      Université de Bourgogne (UB): HAL
    • الموضوع:
    • نبذة مختصرة :
      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 ...
    • Relation:
      hal-03226748; https://u-bourgogne.hal.science/hal-03226748; https://u-bourgogne.hal.science/hal-03226748/document; https://u-bourgogne.hal.science/hal-03226748/file/IC2S2_WithAuthorNames.pdf
    • Rights:
      info:eu-repo/semantics/OpenAccess
    • الرقم المعرف:
      edsbas.136CDFBE