Identifying Influential Nodes Using Overlapping Modularity Vitality

TL;DR

This paper introduces Overlapping Modularity Vitality, a new centrality measure that effectively identifies influential nodes in networks with overlapping communities, outperforming non-overlapping methods in epidemic spreading simulations.

Contribution

It proposes and evaluates Overlapping Modularity Vitality, extending existing measures to account for overlapping community structures in networks.

Findings

Overlapping Modularity Vitality outperforms non-overlapping versions in epidemic spreading.

Using top positive or absolute centrality values improves influence spread.

Incorporating overlapping community knowledge enhances influential node detection.

Abstract

It is of paramount importance to uncover influential nodes to control diffusion phenomena in a network. In recent works, there is a growing trend to investigate the role of the community structure to solve this issue. Up to now, the vast majority of the so-called community-aware centrality measures rely on non-overlapping community structure. However, in many real-world networks, such as social networks, the communities overlap. In other words, a node can belong to multiple communities. To overcome this drawback, we propose and investigate the "Overlapping Modularity Vitality" centrality measure. This extension of "Modularity Vitality" quantifies the community structure strength variation when removing a node. It allows identifying a node as a hub or a bridge based on its contribution to the overlapping modularity of a network. A comparative analysis with its non-overlapping version using the Susceptible-Infected-Recovered (SIR) epidemic diffusion model has been performed on a set of six real-world networks. Overall, Overlapping Modularity Vitality outperforms its alternative. These results illustrate the importance of incorporating knowledge about the overlapping community structure to identify influential nodes effectively. Moreover, one can use multiple ranking strategies as the two measures are signed. Results show that selecting the nodes with the top positive or the top absolute centrality values is more effective than choosing the ones with the maximum negative values to spread the epidemic.