activity
20102022
most citedCharacterizing the Interactions Between Classical and Community-aware Centrality Measures in Complex Networks

63 citations · 194 across the 10 of their papers we have counts for

collaborators

10 papers

cs.SI2022★ 58 cited

Comparative evaluation of community-aware centrality measures

Stephany Rajeh, Marinette Savonnet, Eric Leclercq +1

Influential nodes play a critical role in boosting or curbing spreading phenomena in complex networks. Numerous centrality measures have been proposed for identifying and ranking t…

cs.SI2022★ 7 cited

Identifying Influential Nodes Using Overlapping Modularity Vitality

Stephany Rajeh, Marinette Savonnet, Eric Leclercq +1

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 com…

cs.SI2022★ 8 cited

Comparing Community-aware Centrality Measures in Online Social Networks

Stephany Rajeh, Marinette Savonnet, Eric Leclercq +1

Identifying key nodes is crucial for accelerating or impeding dynamic spreading in a network. Community-aware centrality measures tackle this problem by exploiting the community st…

cs.SI2022★ 3 cited

How Correlated are Community-aware and Classical Centrality Measures in Complex Networks?

Stephany Rajeh, Marinette Savonnet, Eric Leclercq +1

Unlike classical centrality measures, recently developed community-aware centrality measures use a network's community structure to identify influential nodes in complex networks.…

cs.SI2022★ 9 cited

Investigating Centrality Measures in Social Networks with Community Structure

Stephany Rajeh, Marinette Savonnet, Eric Leclercq +1

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 majo…

cs.SI2022★ 2 cited

Modularity-based Backbone Extraction in Weighted Complex Networks

Stephany Rajeh, Marinette Savonnet, Eric Leclercq +1

The constantly growing size of real-world networks is a great challenge. Therefore, building a compact version of networks allowing their analyses is a must. Backbone extraction te…