Centrality in Complex Networks with Overlapping Community Structure

Ghalmane, Zakariya; Cherifi, Chantal; Cherifi, Hocine; Hassouni, Mohammed El

doi:10.1038/s41598-019-46507-y

Cited by 79 publications

(56 citation statements)

References 41 publications

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“…They are assigned an edge trussness t e (e ij ) = 1. The set of associated isolated nodes are also removed V t 1 = {5, 6,7,8,9,10,11,22,23,24, 25} resulting in the 1-truss network G t 1 . The process continues by removing all edges that participate in two triangles and not three triangles are removed.…”

Section: ) K-truss Nested Hierarchymentioning

confidence: 99%

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Interplay Between Hierarchy and Centrality in Complex Networks

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Hierarchy and centrality are two popular notions used to characterize the importance of entities in complex systems. Indeed, many complex systems exhibit a natural hierarchical structure, and centrality is a fundamental characteristic allowing to identify key constituents. Several measures based on various aspects of network topology have been proposed in order to quantify these concepts. While numerous studies have investigated whether centrality measures convey redundant information, how centrality and hierarchy measures are related is still an open issue. In this paper, we investigate the association between centrality and hierarchy using several correlation and similarity evaluation measures. A series of experiments is performed in order to evaluate the combinations of 6 centrality measures with 4 hierarchy measures across 28 diverse real-world networks with varying topological characteristics. Results show that network density and transitivity play a key role in shaping the redundancy between centrality and hierarchy measures.

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Section: ) K-truss Nested Hierarchymentioning

confidence: 99%

“…One can notice that these networks exhibit in general high density (ν), high tran- RBO , and finally RBO at p=0.9 (|RBOp=0.9(α, β)| ≥ 0.7) denoted as λ p=0. 9 RBO . (k)).…”

Section: ) Grouping the Network Based On The Binarized Evaluation Mmentioning

confidence: 99%

Interplay Between Hierarchy and Centrality in Complex Networks

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“…Indeed, overlapping nodes play a highly relevant role in the network due to their ability to reach multiple communities. We recently reported their importance on the epidemic spreading process [29], and how they can be used in order to design effective immunization strategies. Indeed, when an epidemic outbreak occurs, it is not possible to immunize every individual through vaccination due to the limited amount of resources or time.…”

Section: Related Workmentioning

confidence: 99%

“…Community structure is a common property of many realworld networks [29], [36]- [44]. Research on community detection is very active and a plethora of new algorithms based on various definitions are regularly published.…”

Section: B Community Detection Algorithmsmentioning

confidence: 99%

Exploring Hubs and Overlapping Nodes Interactions in Modular Complex Networks

Ghalmane

Cherifi

2020

IEEE Access

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In real-world networks, nodes are usually organized into modules or communities of densely connected nodes. In situations where nodes can belong to multiple communities we say that the communities overlap, and the nodes shared by more than one community are called the overlapping nodes. This occurs especially in social networks where an individual belongs to various social groups and organizations such as working circles, family, friendship or virtual groups on the Internet. Complex networks are known to have a heavy tail degree distribution. Indeed, they are organized with a vast majority of nodes with few interactions and a small set of highly connected nodes called hubs. In this paper, our goal is to study the relationship between the overlapping nodes and the hubs. Indeed, we suspect that the hubs are in the vicinity of the overlapping nodes. If this assumption is confirmed, it gives a new perspective on how the communities are organized and of the crucial importance of the overlapping nodes. In an attempt to investigate the ubiquity of this property, we perform series of experiments on various real-world networks with overlapping community structure. Results show that the hubs represent always a large proportion of the one-step neighbors of overlapping nodes. These results may have implications in various contexts. For example, searching for the hubs in large networks can be done starting from the overlapping nodes. Furthermore, this study may also provide new directions for designing new community detection algorithms. INDEX TERMS Complex networks, community structure, overlapping nodes, hubs.

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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.…”

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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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Centrality in Complex Networks with Overlapping Community Structure

Cited by 79 publications

References 41 publications

Interplay Between Hierarchy and Centrality in Complex Networks

Interplay Between Hierarchy and Centrality in Complex Networks

Exploring Hubs and Overlapping Nodes Interactions in Modular Complex Networks

Extracting backbones in weighted modular complex networks

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