New Betweenness Centrality Node Attack Strategies for Real-World Complex Weighted Networks

In this work, we introduce a new node attack strategy removing nodes with the highest conditional weighted betweenness centrality (CondWBet), which combines the weighted structure of the network and the node’s conditional betweenness. We compare its efficacy with well-known attack strategies from literature over five real-world complex weighted networks. We use the network weighted efficiency (WEFF) like a measure encompassing the weighted structure of the network, in addition to the commonly used binary-topological measure, i.e., the largest connected cluster (LCC). We find that if the measure is WEFF, the CondWBet strategy is the best to decrease WEFF in 3 out of 5 cases. Further, CondWBet is the most effective strategy to reduce WEFF at the beginning of the removal process, whereas the Strength that removes nodes with the highest sum of the link weights first shows the highest efficacy in the final phase of the removal process when the network is broken into many small clusters. These last outcomes would suggest that a better attacking in weighted networks strategy could be a combination of the CondWBet and Strength strategies.

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The heterogeneity in link weights may decrease the robustness of real-world complex weighted networks

Scientific Reports ◽

10.1038/s41598-019-47119-2 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 13

Author(s):

M. Bellingeri ◽

D. Bevacqua ◽

F. Scotognella ◽

D. Cassi

Keyword(s):

Real World ◽

Weighted Networks ◽

Complex Weighted Networks

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Betweenness centrality for temporal multiplexes

Scientific Reports ◽

10.1038/s41598-021-84418-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Silvia Zaoli ◽

Piero Mazzarisi ◽

Fabrizio Lillo

Keyword(s):

Information Flow ◽

Real World ◽

Betweenness Centrality ◽

Temporal Structure ◽

Shortest Paths ◽

Single Layer ◽

Distance Metric ◽

Definition Of

AbstractBetweenness centrality quantifies the importance of a vertex for the information flow in a network. The standard betweenness centrality applies to static single-layer networks, but many real world networks are both dynamic and made of several layers. We propose a definition of betweenness centrality for temporal multiplexes. This definition accounts for the topological and temporal structure and for the duration of paths in the determination of the shortest paths. We propose an algorithm to compute the new metric using a mapping to a static graph. We apply the metric to a dataset of $$\sim 20$$ ∼ 20 k European flights and compare the results with those obtained with static or single-layer metrics. The differences in the airports rankings highlight the importance of considering the temporal multiplex structure and an appropriate distance metric.

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SARCP - Exploiting Cyber-Attack Prediction Through Socially-Aware Recommendation

International Journal of Decision Support System Technology ◽

10.4018/ijdsst.286691 ◽

2022 ◽

Vol 14 (1) ◽

pp. 0-0

Keyword(s):

Social Network ◽

Real World ◽

Cyber Security ◽

Betweenness Centrality ◽

Cyber Attacks ◽

Experimental Results ◽

Cyber Attack ◽

Defence Mechanisms ◽

Network Measure ◽

Recommender Algorithm

In the domain of cyber security, the defence mechanisms of networks has traditionally been placed in a reactionary role. Cyber security professionals are therefore disadvantaged in a cyber-attack situation due to the fact that it is vital that they maneuver such attacks before the network is totally compromised. In this paper, we utilize the Betweenness Centrality network measure (social property) to discover possible cyber-attack paths and then employ computation of similar personality of nodes/users to generate predictions about possible attacks within the network. Our method proposes a social recommender algorithm called socially-aware recommendation of cyber-attack paths (SARCP), as an attack predictor in the cyber security defence domain. In a social network, SARCP exploits and delivers all possible paths which can result in cyber-attacks. Using a real-world dataset and relevant evaluation metrics, experimental results in the paper show that our proposed method is favorable and effective.

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Peer Review #1 of "A GPU-based solution for fast calculation of the betweenness centrality in large weighted networks (v0.2)"

10.7287/peerj-cs.140v0.2/reviews/1 ◽

2017 ◽

Author(s):

AE Sariyuce

Keyword(s):

Peer Review ◽

Betweenness Centrality ◽

Fast Calculation ◽

Weighted Networks

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Analyses of the response of a complex weighted network to nodes removal strategies considering links weight: The case of the Beijing urban road system

Modern Physics Letters B ◽

10.1142/s0217984918500677 ◽

2018 ◽

Vol 32 (05) ◽

pp. 1850067 ◽

Cited By ~ 2

Author(s):

Michele Bellingeri ◽

Zhe-Ming Lu ◽

Davide Cassi ◽

Francesco Scotognella

Keyword(s):

Complex Network ◽

Real World ◽

Road Traffic ◽

Network Efficiency ◽

Weighted Network ◽

Node Importance ◽

Connected Cluster ◽

Binary Network ◽

Binary Networks ◽

Weighted Rank

Complex network response to node loss is a central question in different fields of science ranging from physics, sociology, biology to ecology. Previous studies considered binary networks where the weight of the links is not accounted for. However, in real-world networks the weights of connections can be widely different. Here, we analyzed the response of real-world road traffic complex network of Beijing, the most prosperous city in China. We produced nodes removal attack simulations using classic binary node features and we introduced weighted ranks for node importance. We measured the network functioning during nodes removal with three different parameters: the size of the largest connected cluster (LCC), the binary network efficiency (Bin EFF) and the weighted network efficiency (Weg EFF). We find that removing nodes according to weighted rank, i.e. considering the weight of the links as a number of taxi flows along the roads, produced in general the highest damage in the system. Our results show that: (i) in order to model Beijing road complex networks response to nodes (intersections) failure, it is necessary to consider the weight of the links; (ii) to discover the best attack strategy, it is important to use nodes rank accounting links weight.

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