Robustness of heterogeneous complex networks

Santiago, A.; Benito, R. M.

doi:10.1016/j.physa.2009.02.016

Cited by 15 publications

(9 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The presence of the scale-free property in the degree distribution P(k) seems to be closely related to its tolerance to external or internal perturbations [7][8][9][10][11]. Due to the fact that most of the equipments have low degree k, under a random failure there is a large probability that it affects one of those with few connections.…”

Section: Sdh Network As Graphsmentioning

confidence: 99%

See 1 more Smart Citation

On the robustness of Spanish telecommunication networks

Cárdenas

López

Moyano

et al. 2010

Physica A: Statistical Mechanics and its Applications

Self Cite

View full text Add to dashboard Cite

Section: Sdh Network As Graphsmentioning

confidence: 99%

“…Let us now further analyze the robustness of the SDH network by computing the value of l [11] when the SDH equipment with the highest betweenness is removed. In the left plot of Fig.…”

Section: Effect Of Equipment Failure On the Topological Robustness Ofmentioning

confidence: 99%

On the robustness of Spanish telecommunication networks

Cárdenas

López

Moyano

et al. 2010

Physica A: Statistical Mechanics and its Applications

Self Cite

View full text Add to dashboard Cite

“…In the real world, there exist lots of various entities, and together with their inter-relationships, they can be represented as information networks [1], such as Bibliographic Information Networks [2], Wikipedia [3], and Facebook. Existing studies [4][5][6][7][8][9] mainly focus on representing and analyzing such systems using homogeneous information networks, which composed of one type of nodes and edges. For example, Figure 1a illustrates a co-author homogeneous network consisting of "authors" as nodes and "publishing" relationships as links.…”

Section: Introductionmentioning

confidence: 99%

A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information Networks

Zhang

Jiang

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

There has been increasing interest in the analysis and mining of Heterogeneous Information Networks (HINs) and the classification of their components in recent years. However, there are multiple challenges associated with distinguishing different types of objects in HINs in real-world applications. In this paper, a novel framework is proposed for the weighted Meta graph-based Classification of Heterogeneous Information Networks (MCHIN) to address these challenges. The proposed framework has several appealing properties. In contrast to other proposed approaches, MCHIN can fully compute the weights of different meta graphs and mine the latent structural features of different nodes by using these weighted meta graphs. Moreover, MCHIN significantly enlarges the training sets by introducing the concept of Extension Meta Graphs in HINs. The extension meta graphs are used to augment the semantic relationship among the source objects. Finally, based on the ranking distribution of objects, MCHIN groups the objects into pre-specified classes. We verify the performance of MCHIN on three real-world datasets. As is shown and discussed in the results section, the proposed framework can effectively outperform the baselines algorithms.

show abstract

“…As a result, many issues have been investigated carefully, including the robustness of the topological structure of networks [7][8][9][10][11], the description of cascading phenomenon and transition [12], the protection strategies against cascade [13][14][15][16][17][18], the cost of attack and defense [19,20], and the reliability metrics of networks [21,22]. In addition, the vulnerability of the real-world networks has become an important topic in the design of engineering safety [23,24].…”

Section: Introductionmentioning

confidence: 99%

Cascading Dynamics of Heterogenous Scale-Free Networks with Recovery Mechanism

Jia

et al. 2013

Abstract and Applied Analysis

View full text Add to dashboard Cite

In network security, how to use efficient response methods against cascading failures of complex networks is very important. In this paper, concerned with the highest-load attack (HL) and random attack (RA) on one edge, we define five kinds of weighting strategies to assign the external resources for recovering the edges from cascading failures in heterogeneous scale-free (SF) networks. The influence of external resources, the tolerance parameter, and the different weighting strategies on SF networks against cascading failures is investigated carefully. We find that, under HL attack, the fourth kind of weighting method can more effectively improve the integral robustness of SF networks, simultaneously control the spreading velocity, and control the outburst of cascading failures in SF networks than other methods. Moreover, the third method is optimal if we only knew the local structure of SF networks and the uniform assignment is the worst. The simulations of the real-world autonomous system in, Internet have also supported our findings. The results are useful for using efficient response strategy against the emergent accidents and controlling the cascading failures in the real-world networks.

show abstract

Robustness of heterogeneous complex networks

Cited by 15 publications

References 21 publications

On the robustness of Spanish telecommunication networks

On the robustness of Spanish telecommunication networks

A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information Networks

Cascading Dynamics of Heterogenous Scale-Free Networks with Recovery Mechanism

Contact Info

Product

Resources

About