Central attacks in complex networks: A revisit with new fallback strategy

Lekha, Divya Sindhu; Balakrishnan, Kalpana

doi:10.1016/j.physa.2020.124347

Cited by 18 publications

(11 citation statements)

References 32 publications

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“…Inspired by its application in biology (Seguin et al, 2018), traffic analysis (Scellato et al, 2010), and network attack analysis (Lekha and Balakrishnan, 2020), we use edge betweenness centrality (Das et al, 2018) as the generic (non-topic) measure of navigability. Let α ij be the number of shortest paths from node i to j and α ij (k) is the number of paths passes through the edge k. Then the edge-betweenness centrality is…”

Section: Measures Of For Relative Interestingnessmentioning

confidence: 99%

A Query-Driven System for Discovering Interesting Subgraphs in Social Media

Dasgupta,

Gupta

2021

Preprint

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Social media data are often modeled as heterogeneous graphs with multiple types of nodes and edges. We present a discovery algorithm that first chooses a "background" graph based on a user's analytical interest and then automatically discovers subgraphs that are structurally and content-wise distinctly different from the background graph. The technique combines the notion of a group-by operation on a graph and the notion of subjective interestingness, resulting in an automated discovery of interesting subgraphs. Our experiments on a socio-political database show the effectiveness of our technique.

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Section: Measures Of For Relative Interestingnessmentioning

confidence: 99%

A Query-Driven System for Discovering Interesting Subgraphs in Social Media

Dasgupta,

Gupta

2021

Preprint

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“…In the following formulas, let N represent the number of identified software classes. Degree information in complex network including degree k i , sum of indegree sum k in , sum of outdegree sum k out , and sum of degree sum k [21], respectively, is expressed as follows:…”

Section: Directed Weighted Class Interaction Software Networkmentioning

confidence: 99%

Identifying Key Classes Algorithm in Directed Weighted Class Interaction Network Based on the Structure Entropy Weighted LeaderRank

Jiang

Dai

2020

Mathematical Problems in Engineering

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Identifying key classes can help software maintainers quickly understand software systems. The existing key class recognition algorithms consider the weight of class interaction, but the weight mechanism is single or arbitrary. In this paper, the multitype weighting mechanism is considered, and the key classes are accurately identified by using four kinds of interaction. By abstracting the software system into the directed weighted class interaction network, a novel Structure Entropy Weighted LeaderRank of identifying key classes algorithm is proposed. First, considering multiple types and directions of interactions between every pair of classes, the directed weighted class interaction software network (DWCIS-Network) is built. Second, Class Entropy of each class is initialized by the software structural entropy in DWCIS-Network; the Structure Entropy Weighted LeaderRank applies the biased random walk process to iterate Class Entropy. Finally, the iteration is completed to obtain the Final Class Entropy ( FCE ) of each class as the importance score of each class, top- k classes are obtained, and key classes are identified. For two sets of experiments on Ant and JHotDraw, our approach effectively identifies key classes in class-level software networks for different top- k of classes, and the recall rates of our approach are the highest, 80% and 100%, respectively. From top-15% to top-5%, the precision of our approach is improved by 13.39%, which is the highest in comparison with the precisions of the other two classical approaches. Compared with the best performance of the two classical approaches, the RankingScore of our approach is improved by 16.51% in JHotDraw.

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“…Lekha et al confirmed the relevance of closeness centrality in central attacks [6]. Wang et al demonstrated that a critical-point before which damage attack is more destructive than degree attack for real networks in a wide range of domains [7].…”

Section: Introductionmentioning

confidence: 97%

Model and Analyze the Cascading Failure of Scale-Free Network Considering the Selective Forwarding Attack

et al. 2021

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Many real-world networks have scale-free characteristics and can be abstracted into scalefree networks. Aiming at the problem that scale-free networks have low fault tolerance in the face of malicious attacks, we focus on the selective forwarding attack behavior that exists widely in the networks, and build a selective forwarding attack model based on node importance. Moreover, according to the neighbor node's malicious and non-malicious behavior, a load redistribution strategy for failed node is proposed. Then, the network's damage degree is given to evaluate the comprehensive impact of cascading failure phenomenon on network connectivity and information integrity under selective forwarding attack. Finally, a cascading failure model of scale-free networks considering selective forwarding attack behavior is obtained. Based on this model, the propagation condition without triggering network cascading failure, the selective forwarding attack intensity threshold and the load loss ratio are obtained. By simulation on the classical BA scale-free network model, the results show that multiple nodes' random failure occurs in scalefree networks, selective forwarding attack behavior is helpful to improve the connectivity of the network. Besides, the network exists selective forwarding attack intensity threshold, when attack intensity is greater than the intensity threshold, the malicious nodes will not fail because of the failure of the neighbor nodes. But, selective forwarding attack behavior can destroy the information integrity, and there is a negative correlation between the attack intensity and the information integrity. These results have certain guiding significance for cascade failure analysis and prevention method research and design in real life. INDEX TERMS Scale-free network, cascading failure, selective forwarding attack, attack intensity threshold, information integrity This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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Central attacks in complex networks: A revisit with new fallback strategy

Cited by 18 publications

References 32 publications

A Query-Driven System for Discovering Interesting Subgraphs in Social Media

A Query-Driven System for Discovering Interesting Subgraphs in Social Media

Identifying Key Classes Algorithm in Directed Weighted Class Interaction Network Based on the Structure Entropy Weighted LeaderRank

Model and Analyze the Cascading Failure of Scale-Free Network Considering the Selective Forwarding Attack

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