0000000001158875
AUTHOR
Zakariya Ghalmane
showing 17 related works from this author
Community-based method for extracting backbones
2022
Networks are an adequate representation for modeling and analyzing a great variety of complex systems. However, understanding networks with millions of nodes and billions of connections can be pretty challenging due to memory and time constraints. Therefore, selecting the relevant nodes and edges of these large-scale networks while preserving their core information is a major issue. In most cases, the so-called backbone extraction methods are based either on coarse-graining or filtering approaches. Coarse-graining techniques reduce the network size by gathering similar nodes into super-nodes, while filter-based methods eliminate nodes or edges according to a statistical property. In this wo…
A Community-Aware Backbone Extractor for Weighted Networks
2020
International audience
Finding Influential Nodes in Networks with Community Structure
2020
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 …
Influential Spreaders in Networks with Community Structure
2020
International audience; Hassouni (2019). Centrality in Complex Networks with overlapping Community structure. Scientific Reports, 9(1).
Characterizing the Relation between Hubs and Overlapping Nodes in Modular Networks
2019
International audience
Influential Spreaders in Modular Networks
2020
International audience; Hassouni (2019). Centrality in Complex Networks with overlapping Community structure. Scientific Reports, 9(1).
Extracting modular-based backbones in weighted networks
2021
Abstract Networks are an adequate representation for modeling and analyzing a great variety of complex systems. However, understanding networks with millions of nodes and billions of connections can be pretty challenging due to memory and time constraints. Therefore, selecting the relevant nodes and edges of these large-scale networks while preserving their core information is a major issue. In most cases, the so-called backbone extraction methods are based either on coarse-graining or filtering approaches. Coarse-graining techniques reduce the network size by gathering similar nodes into super-nodes, while filter-based methods eliminate nodes or edges according to a statistical property.In…
Searching for Influential Nodes in Modular Networks
2020
International audience
Localization of Hubs in Modular Networks
2020
International audience
A backbone extraction method for complex weighted networks
2020
International audience
A stochastic approach for extracting community-based backbones
2022
Large-scale dense networks are very parvasive in various fields such as communication, social analytics, architecture, bio-metrics, etc. Thus, the need to build a compact version of the networks allowing their analysis is a matter of great importance. One of the main solutions to reduce the size of the network while maintaining its characteristics is backbone extraction techniques. Two types of methods are distinguished in the literature: similar nodes are gathered and merged in coarse-graining techniques to compress the network, while filter-based methods discard edges and nodes according to some statistical properties. In this paper, we propose a filtering-based approach which is based on…
k-Truss Decomposition for Modular Centrality
2018
There is currently much interest in identifying influential spreaders in complex networks due to many applications concerned, such as controlling the outbreak of epidemics and conducting advertisements for commercial products, and so on. A plethora of centrality measures have been proposed over the years based on the topological properties of networks. However, most of these classical centrality measures fail to select the most influential nodes in networks with a modular structure despite that it is an omnipresent property in real-world networks. Few authors have introduced centrality measures tailored to networks with community structure. In a recent work, we have shown that, in this case…
Extracting Backbones in Weighted Modular Complex Networks
2020
AbstractNetwork science provides effective tools to model and analyze complex systems. However, the increasing size of real-world networks becomes a major hurdle in order to understand their structure and topological features. Therefore, mapping the original network into a smaller one while preserving its information is an important issue. Extracting the so-called backbone of a network is a very challenging problem that is generally handled either by coarse-graining or filter-based methods. Coarse-graining methods reduce the network size by grouping similar nodes, while filter-based methods prune the network by discarding nodes or edges based on a statistical property. In this paper, we pro…
Centrality in Complex Networks with Overlapping Community Structure
2019
AbstractIdentifying influential spreaders in networks is an essential issue in order to prevent epidemic spreading, or to accelerate information diffusion. Several centrality measures take advantage of various network topological properties to quantify the notion of influence. However, the vast majority of works ignore its community structure while it is one of the main features of many real-world networks. In a recent study, 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 the nodes belonging to the other communities. Using global and local co…
Betweenness Centrality for Networks with Non-Overlapping Community Structure
2018
Evaluating the centrality of nodes in complex networks is one of the major research topics being explored due to its wide range of applications. Among the various measures that have been developed over the years, Betweenness centrality is one of the most popular. Indeed, it has proved to be efficient in many real-world situations. In this paper, we propose an extension of the Betweenness centrality designed for networks with nonoverlapping community structure. It is a linear combination of the so-called “local” and “global” Betweenness measures. The Local measure takes into account the influence of a node at the community level while the global measure depends only on the interactions betwe…
Localization of hubs in complex networks with overlapping modular structure
2020
International audience
Interactions between overlapping nodes and hubs in complex networks with modular structure
2020
International audience