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

63 citations · 193 across the 9 of their papers we have counts for

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8 papers · 1 filter

cs.SI202258 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.SI20227 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.SI20228 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.SI20223 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.SI20229 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.SI20222 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…