Centrality measures for networks with community structure

Gupta, Navneet; Singh, Anurag; Cherifi, Hocine

doi:10.1016/j.physa.2016.01.066

Cited by 76 publications

(45 citation statements)

References 34 publications

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“…Comm strategy was proposed by Gupta et al [96] [97]. The aim of this strategy is to target nodes that are at the same time hubs in their communities and bridges towards other communities.…”

Section: Global and Local Strategiesmentioning

confidence: 99%

On community structure in complex networks: challenges and opportunities

et al. 2019

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Community structure is one of the most relevant features encountered in numerous real-world applications of networked systems. Despite the tremendous effort of a large interdisciplinary community of scientists working on this subject over the past few decades to characterize, model, and analyze communities, more investigations are needed in order to better understand the impact of their structure and dynamics on networked systems. Here, in the first section, we review the work on generative models of communities and their role in developing strong foundation for community detection algorithms. We discuss modularity and algorithms based on modularity maximization. Then we follow with an overview of the Stochastic Block Model and its different variants as well as inference of communities structures from the model. The following section focuses on time evolving networks, where existing nodes and links can disappear, and in parallel new nodes and links may be introduced. The extraction of communities under such circumstances poses an interesting and non-trivial problem that has gained considerable interest over the last decade. We briefly discuss considerable advances made in this field recently. In the last section, we discuss immunization strategies essential for targeting the influential spreaders of epidemics in modular networks. Their main goal is to select and immunize a small proportion of individuals from the whole network to control the diffusion process. Various strategies have emerged over the years suggesting different ways to immunize nodes in networks with overlapping and non-overlapping community structure. We first discuss stochastic strategies that require little or no information about the network topology at the expense of their performance. Then, we introduce deterministic strategies that have proven to be very efficient in controlling the epidemic outbreaks, but require complete knowledge of the network.

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“…Comm strategy was proposed by Gupta et al [96] [97]. The aim of this strategy is to target nodes that are at the same time hubs in their communities and bridges towards other communities.…”

Section: Global and Local Strategiesmentioning

confidence: 99%

On community structure in complex networks: challenges and opportunities

et al. 2019

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“…-Comm strategy: N. Gupta et al [15] proposed a strategy called the Comm measure of a node. It combines the number of its intra-community links (links with nodes inside its community) and the number of its inter-community links (links with nodes outside its community) to rank nodes that are both hubs in their community and bridges between communities.…”

Section: Deterministic Immunization Strategiesmentioning

confidence: 99%

Immunization of networks with non-overlapping community structure

Ghalmane

Hassouni

Cherifi

2019

Soc. Netw. Anal. Min.

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Although community structure is ubiquitous in complex networks, few works exploit this topological property to control epidemics. In this work, devoted to networks with non-overlapping community structure (i.e, a node belongs to a single community), we propose and investigate three deterministic immunization strategies. In order to characterize the influence of a node, various pieces of information are used such as the number of communities that the node can reach in one hop, the nature of the links (intra community links, inter community links), the size of the communities, and the interconnection density between communities. Numerical simulations with the Susceptible-Infected-Removed (SIR) epidemiological model are conducted on both real-world and synthetic networks. Experimental results show that the proposed strategies are more effective than classical deterministic alternatives that are agnostic of the community structure. Additionally, they outperform stochastic and deterministic strategies designed for modular networks.

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“…Many notions of centrality coexist for graphs [4,5], multilayer graphs [12,22] and stream graphs [6,16] alike. As of today, no consensus emerges on a global centrality notion, as they all capture different notions of importance [13].…”

Section: Centralitiesmentioning

confidence: 99%

Introducing Multilayer Stream Graphs and Layer Centralities

Parmentier

Viard

Renoust

et al. 2019

Complex Networks and Their Applications VIII

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Graphs are commonly used in mathematics to represent some relationships between items. However, as simple objects, they sometimes fail to capture all relevant aspects of real-world data. To address this problem, we generalize them and model interactions over time with multilayer structure. We build and test several centralities to assess the importance of layers of such structures. In order to showcase the relevance of this new model with centralities, we give examples on two large-scale datasets of interactions, involving individuals and flights, and show that we are able to explain subtle behaviour patterns in both cases.We now introduce the concept of multilayer stream graph. To illustrate our definitions, we will use a toy example of a population of monkeys F 1 , F 2 , M 1 , M 2 (two females and two males) interacting together.

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Centrality measures for networks with community structure

Cited by 76 publications

References 34 publications

On community structure in complex networks: challenges and opportunities

On community structure in complex networks: challenges and opportunities

Immunization of networks with non-overlapping community structure

Introducing Multilayer Stream Graphs and Layer Centralities

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