0000000000193216

AUTHOR

Stephany Rajeh

A comparison of community-aware centrality measures in online social networks

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Investigating the Relationship Between Community-aware and Classical Centrality Measures

International audience

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A Modularity Backbone Extraction Method for Weighted Complex Networks

The exponential growth in the size of real-world networks is a major barrier to analyzing their structure and dynamics. Thus, reducing the network's size while maintaining its topological features is highly significant. As community structure is one of the fundamental fingerprints of real-world networks, this work proposes a new node-filtering backbone extraction method to preserve the network's community structure.

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An empirical study on classical and community-aware centrality measures in complex networks

Community structure is a ubiquitous feature in natural and artificial systems. Identifying key nodes is a fundamental task to speed up or mitigate any diffusive processes in these systems. Centrality measures aim to do so by selecting a small set of critical nodes. Classical centrality measures are agnostic to community structure, while community-aware centrality measures exploit this property. Several works study the relationship between classical centrality measures, but the relationship between classical and community-aware centrality measures is almost unexplored. In this work [1], we answer two questions: (1) How do classical and community-aware centrality measures relate? (2) What is …

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Finding Influential Nodes in Networks with Community Structure

International audience; Identifying influential nodes is a fundamental issue in complex networks. Several centrality measures take advantage of various network topological properties to target the top spreaders. However, the vast majority of works ignore its community structure while it is one of the main properties of many real-world networks. In our previous work 4 , we show that the centrality of a node in a network with non-overlapping communities depends on two features: Its local influence on the nodes belonging to its community, and its global influence on nodes belonging to the other communities. For this end, we introduced a framework to adapt all the classical centrality measures …

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

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…

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How Correlated Are Community-Aware and Classical Centrality Measures in Complex Networks?

Unlike classical centrality measures, recently developed community-aware centrality measures use a network’s community structure to identify influential nodes in complex networks. This paper investigates their relationship on a set of fifty real-world networks originating from various domains. Results show that classical and community-aware centrality measures generally exhibit low to medium correlation values. These results are consistent across networks. Transitivity and efficiency are the most influential macroscopic network features driving the correlation variation between classical and community-aware centrality measures. Additionally, the mixing parameter, the modularity, and the Max…

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Identifying Influential Nodes: The Overlapping Modularity Vitality Framework

This paper proposes an Overlapping Modularity Vitality framework for identifying influential nodes in networks with overlapping community structures. The framework uses a generalized modularity equation and the concept of vitality to calculate the centrality of a node. We investigate three definitions of overlapping modularity and three ranking strategies prioritizing hubs, bridges, or both types of nodes. Experimental investigations involving real-world networks show that the proposed framework demonstrates the benefit of incorporating overlapping community structure information to identify critical nodes in a network.

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An Empirical Comparison of Centrality and Hierarchy Measures in Complex Networks

International audience

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Impact of the community structure on the dynamics of complex networks

Networks are everywhere. We encounter them daily in our lives, through our social interactions, how we come up with decisions in our brain, to having phone calls, conducting financial transactions, and traveling from one place to another. Individual actions are influenced by their environment, which is, in turn, influenced by the network's topology. Notably, individuals may change their actions, ideas, or opinions to conform to the aspirations of a particular social group. In the same vein, the spread of a virus can take a certain course if the network's structure induces specific pathways for expansion. In such scenarios, communities substantially impact the evolution of the dynamics. They…

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Assessing the Relationship Between Centrality and Hierarchy in Complex Networks

International audience

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Investigating Centrality Measures in Social Networks with Community Structure

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 majority of classical centrality measures are agnostic of the community structure characterizing many social networks. Recent works have developed community-aware centrality measures that exploit features of the community structure information encountered in most real-world complex networks. In this paper, we investigate the interactions between 5 popular classical centrality measures and 5 community-aware centrality measures using 8 real-world online networks. Correlation as well as similarity measures between both t…

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Classical versus Community-aware Centrality Measures: An Empirical Study

International audience

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Hierarchy and Centrality: Two Sides of The Same Coin?

International audience

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