Impact of Community Structure on Network Efficiency and Communicability

Wang, Gaoxia; Qin, Taigui

doi:10.1109/icicta.2010.665

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…That is, those properties that characterizes the network as a whole. There have been several works analyzing the role of communicability and self-communicability at these three different scales (Bradonjić et al, 2011;Crofts and Higham, 2009;Crofts et al, 2011;da Fontoura Costa et al, 2008;Došlić, 2005;Estrada 2007a;2010b;Estrada and Bodin, 2008;Estrada and Hatano, 2009a;Estrada et al, 2009;Jungsbluth et al, 2007;Koponen and Pehkonen, 2010;Li et al, 2010;MacArthur et al, 2010;Malliaros and Megalooikonomou, 2011;Ren et al, 2011;Tordesillas et al, 2010;Shang, 2011b;Wang and Qin, 2010;Walker and Tordesillas, 2010;Ying and Wu, 2008). We present here some illustrative examples to give a flavor of the relevance of these approaches.…”

Section: Communicability and The Analysis Of Networkmentioning

confidence: 98%

The physics of communicability in complex networks

2012

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a b s t r a c tA fundamental problem in the study of complex networks is to provide quantitative measures of correlation and information flow between different parts of a system. To this end, several notions of communicability have been introduced and applied to a wide variety of real-world networks in recent years. Several such communicability functions are reviewed in this paper. It is emphasized that communication and correlation in networks can take place through many more routes than the shortest paths, a fact that may not have been sufficiently appreciated in previously proposed correlation measures. In contrast to these, the communicability measures reviewed in this paper are defined by taking into account all possible routes between two nodes, assigning smaller weights to longer ones. This point of view naturally leads to the definition of communicability in terms of matrix functions, such as the exponential, resolvent, and hyperbolic functions, in which the matrix argument is either the adjacency matrix or the graph Laplacian associated with the network.Considerable insight on communicability can be gained by modeling a network as a system of oscillators and deriving physical interpretations, both classical and quantummechanical, of various communicability functions. Applications of communicability measures to the analysis of complex systems are illustrated on a variety of biological, physical and social networks. The last part of the paper is devoted to a review of the notion of locality in complex networks and to computational aspects that by exploiting sparsity can greatly reduce the computational efforts for the calculation of communicability functions for large networks.

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Section: Communicability and The Analysis Of Networkmentioning

confidence: 98%

The physics of communicability in complex networks

2012

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“…Uncovering the relations between the macroscopic topological properties and the community structure of networks has also attracted many researchers [16,21,27,41,43]. For example, Nematzadeh et al [21] show that a strong community structure enhances information diffusion under the linear threshold model through intra-community links.…”

Section: Related Workmentioning

confidence: 99%

“…Orman et al [27] demonstrate the positive relationship between the community structure strength and transitivity. The work of Wang et al [41] illustrates that communicability is more difficult in networks with a welldefined community structure. Wharrie et al [43] show that the number of communities naturally increases when networks exhibit high clustering.…”

Section: Related Workmentioning

confidence: 99%

Comparative evaluation of community-aware centrality measures

Rajeh,

Savonnet,

Leclercq

et al. 2022

Preprint

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Influential nodes play a critical role in boosting or curbing spreading phenomena in complex networks. Numerous centrality measures have been proposed for identifying and ranking the nodes according to their importance. Classical centrality measures rely on various local or global properties of the nodes. They do not take into account the network community structure. Recently, a growing number of researches have shifted to community-aware centrality measures. Indeed, it is a ubiquitous feature in a vast majority of real-world networks. In the literature, the focus is on designing community-aware centrality measures. However, up to now, there is no systematic evaluation of their effectiveness. This study fills this gap. It allows answering which community-aware centrality measure should be used in practical situations. We investigate seven influential communityaware centrality measures in an epidemic spreading process scenario using the Susceptible-Infected-Recovered (SIR) model on a set of fifteen real-world networks. Results show that generally, the correlation between community-aware centrality measures is low. Furthermore, in a multiple-spreader problem, when resources are available, targeting distant hubs using Modularity Vitality is more effective. However, with limited resources, diffusion expands better through bridges, especially in networks with a medium or strong community structure.

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“…The work by Lancichinetti et al [31] show that a set of mesoscopic characteristics such as community size distribution and average path length of networks originating from the same domain are relatively similar. A paper by Wang et al [32] reveals that as the community structure strength increases, the communicability in a network decreases. According to the work of Nematzadeh, et al [33], the emergence of a global diffusion occurs after reaching a minimum threshold of community structure strength.…”

Section: Introductionmentioning

confidence: 99%

Characterizing the Interactions Between Classical and Community-aware Centrality Measures in Complex Networks

Rajeh,

Savonnet,

Leclercq

et al. 2021

Preprint

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Identifying vital nodes in networks exhibiting a community structure is a fundamental issue. Indeed, community structure is one of the main properties of real-world networks. Recent works have shown that community-aware centrality measures compare favorably with classical measures agnostic about this ubiquitous property. Nonetheless, there is no clear consensus about how they relate and in which situation it is better to use a classical or a community-aware centrality measure. To this end, in this paper, we perform an extensive investigation to get a better understanding of the relationship between classical and community-aware centrality measures reported in the literature. Experiments use artificial networks with controlled community structure properties and a large sample of real-world networks originating from various domains. Results indicate that the stronger the community structure, the more appropriate the community-aware centrality measures. Furthermore, variations of the degree and community size distribution parameters do not affect the results. Finally, network transitivity and community structure strength are the most significant drivers controlling the interactions between classical and community-aware centrality measures.

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Impact of Community Structure on Network Efficiency and Communicability

Cited by 6 publications

References 13 publications

The physics of communicability in complex networks

The physics of communicability in complex networks

Comparative evaluation of community-aware centrality measures

Characterizing the Interactions Between Classical and Community-aware Centrality Measures in Complex Networks

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