User and group networks on YouTube: A comparative analysis

Jebabli, Malek; Cherifi, Hocine; Cherifi, Chantal; Hamouda, Atef

doi:10.1109/aiccsa.2015.7507126

Cited by 14 publications

(6 citation statements)

References 13 publications

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“…They are used by many researchers in numerous domains ranging from technology to biology [3][4][5][6][7][8] . Extensive attention has been paid to reveal the common characteristics of complex networks, such as the scale-free behavior of the degree distribution 9 , the small-world property 10 and the community structure [11][12][13][14][15][16] . However, with the growing size of real-world systems, extracting relevant features and information becomes a much harder task to achieve.…”

mentioning

confidence: 99%

Extracting backbones in weighted modular complex networks

Ghalmane¹,

Cherifi²,

Cherifi³

2020

Sci Rep

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Network 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 propose and investigate two filter-based methods exploiting the overlapping community structure in order to extract the backbone in weighted networks. Indeed, highly connected nodes (hubs) and overlapping nodes are at the heart of the network. In the first method, called “overlapping nodes ego backbone”, the backbone is formed simply from the set of overlapping nodes and their neighbors. In the second method, called “overlapping nodes and hubs backbone”, the backbone is formed from the set of overlapping nodes and the hubs. For both methods, the links with the lowest weights are removed from the network as long as a backbone with a single connected component is preserved. Experiments have been performed on real-world weighted networks originating from various domains (social, co-appearance, collaboration, biological, and technological) and different sizes. Results show that both backbone extraction methods are quite similar. Furthermore, comparison with the most influential alternative filtering method demonstrates the greater ability of the proposed backbones extraction methods to uncover the most relevant parts of the network.

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confidence: 99%

Extracting backbones in weighted modular complex networks

Ghalmane¹,

Cherifi²,

Cherifi³

2020

Sci Rep

Self Cite

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“…Nonetheless, nodes can belong to several modules. Indeed, many networks, such as social, collaboration, biological, and infrastructural networks, are characterized by an overlapping community structure [4]- [7]. In these networks, the overlapping nodes may have multiple functions.…”

Section: Introductionmentioning

confidence: 99%

Identifying influential nodes using overlapping modularity vitality

Rajeh

Savonnet

Leclercq

et al. 2021

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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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 community structure to solve this issue. Up to now, the vast majority of the so-called community-aware centrality measures rely on non-overlapping community structure. However, in many real-world networks, such as social networks, the communities overlap. In other words, a node can belong to multiple communities. To overcome this drawback, we propose and investigate the "Overlapping Modularity Vitality" centrality measure. This extension of "Modularity Vitality" quantifies the community structure strength variation when removing a node. It allows identifying a node as a hub or a bridge based on its contribution to the overlapping modularity of a network. A comparative analysis with its non-overlapping version using the Susceptible-Infected-Recovered (SIR) epidemic diffusion model has been performed on a set of six real-world networks. Overall, Overlapping Modularity Vitality outperforms its alternative. These results illustrate the importance of incorporating knowledge about the overlapping community structure to identify influential nodes effectively. Moreover, one can use multiple ranking strategies as the two measures are signed. Results show that selecting the nodes with the top positive or the top absolute centrality values is more effective than choosing the ones with the maximum negative values to spread the epidemic.

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“…Recent works tend to combine both local and global measures [5,6]. Real-world OSNs often exhibit a community structure in which groups of nodes are closely connected to each other and sparsely connected to nodes in other communities [7,8]. Community structure has major implications on the dynamics of the network [9].…”

Section: Introductionmentioning

confidence: 99%

Investigating Centrality Measures in Social Networks with Community Structure

Rajeh¹,

Savonnet²,

Leclercq³

et al. 2020

Studies in Computational Intelligence

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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 type of centrality measures are computed. Results show that community-aware centrality measures can be divided into two groups. The first group, which includes Bridging centrality, Community Hub-Bridge and Participation Coefficient, provides distinctive node information as compared to classical centrality. This behavior is consistent across the networks. The second group which includes Community-based Mediator and Number of Neighboring Communities is characterized by more mixed results that vary across networks.

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User and group networks on YouTube: A comparative analysis

Cited by 14 publications

References 13 publications

Extracting backbones in weighted modular complex networks

Extracting backbones in weighted modular complex networks

Identifying influential nodes using overlapping modularity vitality

Investigating Centrality Measures in Social Networks with Community Structure

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