0000000001258612
AUTHOR
Chantal Cherifi
showing 27 related works from this author
A Community-Aware Backbone Extractor for Weighted Networks
2020
International audience
Overlapping Community Structure in Co-authorship Networks: A Case Study
2014
Community structure is one of the key properties of real-world complex networks. It plays a crucial role in their behaviors and topology. While an important work has been done on the issue of community detection, very little attention has been devoted to the analysis of the community structure. In this paper, we present an extensive investigation of the overlapping community network deduced from a large-scale co-authorship network. The nodes of the overlapping community network represent the functional communities of the co-authorship network, and the links account for the fact that communities share some nodes in the co-authorship network. The comparative evaluation of the topological prop…
User and group networks on YouTube: A comparative analysis
2015
International audience; YouTube is the largest video-sharing social network where users (aka channels) can create links to any other users. Moreover, initially, users were allowed to create and join special groups of interest. Therefore, two types of online social networks can be defined. First, a user network where the nodes represent the users and the edges represent the social ties (friendship) between users. Second, a group network where the nodes represent the groups and the edges represent the social ties between groups, due to shared users. As the group network can be apprehended as the ground-truth overlapping community graph (where the nodes are the discovered communities and the l…
Community detection algorithm evaluation with ground-truth data
2018
International audience; Community structure is of paramount importance for the understanding of complex networks. Consequently, there is a tremendous effort in order to develop efficient community detection algorithms. Unfortunately, the issue of a fair assessment of these algorithms is a thriving open question. If the ground-truth community structure is available, various clustering-based metrics are used in order to compare it versus the one discovered by these algorithms. However, these metrics defined at the node level are fairly insensitive to the variation of the overall community structure. To overcome these limitations, we propose to exploit the topological features of the ‘communit…
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
Overlapping community detection versus ground-truth in AMAZON co-purchasing network
2015
International audience; Objective evaluation of community detection algorithms is a strategic issue. Indeed, we need to verify that the communities identified are actually the good ones. Moreover, it is necessary to compare results between two distinct algorithms to determine which is most effective. Classically, validations rely on clustering comparison measures or on quality metrics. Although, various traditional performance measures are used extensively. It appears very clearly that they cannot distinguish community structures with different topological properties. It is therefore necessary to propose an alternative methodology more sensitive to the community structure variations in orde…
A backbone extraction method for complex weighted networks
2020
International audience
Exploring The Mesoscopic Structure Of The World Air Transportation Network
2020
International audience; Air transportation networks have been extensively studied in the network science literature. Researchers focus on airlines networks, national, regional, continental and worldwide networks using monoplex or multiplex approaches. Inspired by recent results on community-aware centrality measures [1], in this work, an extensive analysis of the macroscopic, mesoscopic and microscopic topological properties of the world air transportation network is performed. Based on the community structure uncovered by the Louvain algorithm, the original network is split into local components and global components. The local components are made of the communities by removing the interco…
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…
Interactions between overlapping nodes and hubs in complex networks with modular structure
2020
International audience
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…
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).
Complex Networks & Their Applications VII
2018
International audience
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).
Localization of Hubs in Modular Networks
2020
International audience
SIMILARITY NETWORK FOR SEMANTIC WEB SERVICES SUBSTITUTION
2013
Web services substitution is one of the most challenging tasks for automating the composition process of multiple Web services. It aims to improve performances and to deal efficiently with Web services failures. Many existing solutions have approached the problem through classification of substitutable Web services. To go a step further, we propose in this paper a network based approach where nodes are Web services operations and links join similar operations. Four similarity measures based on the comparison of input and output parameters values of Web services operations are presented. A comparative evaluation of the topological structure of the corresponding networks is performed on a ben…
Centrality in Networks with Overlapping Communities
2018
International audience
On Topological Structure of Web Services Networks for Composition. In Int. Journal of Web Engineering and Technology
2013
In order to deal efficiently with the exponential growth of the Web services landscape in composition life cycle activities, it is necessary to have a clear view of its main features. As for many situations where there is a lot of interacting entities, the complex networks paradigm is an appropriate approach to analyze the interactions between the multitudes of Web services. In this paper, we present and investigate the main interactions between semantic Web services models from the complex network perspective. Results show that both parameter and operation networks exhibit the main characteristics of typical real-world complex networks such as the "small-world" property and an inhomogeneou…
Evaluating Community Detection Algorithms: A multidimensional issue
2018
International audience; na
Localization of hubs in complex networks with overlapping modular structure
2020
International audience
On local and global components of the air transportation network
2020
International audience