Characterizing the interactions between classical and community-aware centrality measures in complex networks

Abstract

Identifying vital nodes in networks exhibiting a community structure is a fundamental issue. Indeed, community structure is one of the main properties of real-world networks. Recent works have shown that community-aware centrality measures compare favorably with classical measures agnostic about this ubiquitous property. Nonetheless, there is no clear consensus about how they relate and in which situation it is better to use a classical or a community-aware centrality measure. To this end, in this paper, we perform an extensive investigation to get a better understanding of the relationship between classical and community-aware centrality measures reported in the literature. Experiments use artificial networks with controlled community structure properties and a large sample of real-world networks originating from various domains. Results indicate that the stronger the community structure, the more appropriate the community-aware centrality measures. Furthermore, variations of the degree and community size distribution parameters do not affect the results. Finally, network transitivity and community structure strength are the most significant drivers controlling the interactions between classical and community-aware centrality measures.

Global and local community memberships for estimating spreading capability of nodes in social networks

“…Particularly, the calculation of centrality measures (e.g., degree, betweenness) in a differentiated manner to reflect a node’s local and global influence (Rajeh et al. 2021 ; Ghalmane et al. 2019b ) was shown to lead to a more accurate prediction of efficient spreaders.…”

“…A particularly important topological feature which influences the efficiency of the spreading process is the community structure of networks and the resulting properties of nodes (Rajeh et al. 2021 ; Ghalmane et al. 2019a , b ; Kitsak et al.…”

“…Apart from hubs, the community structure of graphs is another important factor influencing spreading processes (Rajeh et al. 2021 ; Ghalmane et al. 2019a , b ).…”

“…Such properties can be discovered by using local information on the node level or by examining the global network structure. A particularly important topological feature which influences the efficiency of the spreading process is the community structure of networks and the resulting properties of nodes (Rajeh et al 2021;Ghalmane et al 2019a, b;Kitsak et al 2010). To this end, Kitsak et al (2010) showed that the most efficient spreaders within a network are not necessarily the most central nodes (i.e.…”

“…This is also reflected by the fact that an uneven degree distribution within networks (many hubs) results in more efficient spreading (Barabasi 2016). Apart from hubs, the community structure of graphs is another important factor influencing spreading processes (Rajeh et al 2021;Ghalmane et al 2019a, b). Information spreads more easily within highly connected sub-communities (Stegehuis et al 2016), and similarly, nodes who act as bridges or are members of multiple overlapping communities might also spread information more easily between communities.…”