Event stream controllability on event-based complex networks

Arebi, Peyman; Fatemi, Afsaneh; Ramezani, Reza

doi:10.1016/j.eswa.2022.118886

Cited by 28 publications

(4 citation statements)

References 29 publications

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“…The higher-order model has greatly reduced the data overhead of the network by summarizing the data and storing essential network information at higher abstract levels [16]. Another category of proposed network models is models based on the tree structure [1], [6], [7], [17], [18]. In controllability methods based on the tree model, faster algorithms are used on trees instead of very slow graph algorithms.…”

Section: 𝑥(𝑡 + 1) = 𝐴(𝑡) 𝑥(𝑡) + 𝐵(𝑡) 𝑢(T)mentioning

confidence: 99%

“…In temporal networks, the closeness centrality of a node is equal to dividing one by the total length of the shortest path from that node to other network nodes. The closeness centrality of the node in temporal networks is obtained as (6): (6) where:…”

Section: • Node Closeness Centrality In Temporal Networkmentioning

confidence: 99%

“…In the layer model, all the nodes are repeated in different layers and are connected using links between the layers. The controllability methods based on the layer model also have many problems [6]. The most important of them are: 1. very low execution speed of controllability processes, 2. very high data overhead.…”

Section: 𝑥(𝑡 + 1) = 𝐴(𝑡) 𝑥(𝑡) + 𝐵(𝑡) 𝑢(T)mentioning

confidence: 99%

“…In controllability methods based on the tree model, faster algorithms are used on trees instead of very slow graph algorithms. One of the most prominent proposed methods based on the tree model is the method proposed in [6]. In this method, a network representation model is proposed in which each node has an MCSTree.…”

Section: 𝑥(𝑡 + 1) = 𝐴(𝑡) 𝑥(𝑡) + 𝐵(𝑡) 𝑢(T)mentioning

confidence: 99%

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Dynamical Immunization of Data Network Controllability Processes Against Centrality Attacks on Temporal Networks

Arebi

2024

Preprint

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The main goal of controllability network methods on temporal networks is to control all nodes with the minimum number of control nodes. Real-world temporal networks are faced with many errors and attacks that cause the network structure to be changed in some way so that the controllability processes are disturbed and the network is out of fully controllability mode. One of the most important attacks on temporal networks is centrality attacks. In this paper, the types of centrality attacks and their destructive effects on the controllability of temporal networks have been investigated. To deal with centrality attacks, strategies have been proposed on dynamic graph by adding new control nodes or adding new links to the network so that the network is protected against centrality attacks. The results of simulation and comparing them with conventional methods demonstrate that the proposed node addition strategy has performed better than other methods and the improvement rate in terms of execution time is 60%. On the other hand, the proposed immunization strategy by adding links has kept the network controllable with a smaller number of links (38%) and less execution time (52%) compared to other methods.

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Section: 𝑥(𝑡 + 1) = 𝐴(𝑡) 𝑥(𝑡) + 𝐵(𝑡) 𝑢(T)mentioning

confidence: 99%

Section: • Node Closeness Centrality In Temporal Networkmentioning

confidence: 99%

Section: 𝑥(𝑡 + 1) = 𝐴(𝑡) 𝑥(𝑡) + 𝐵(𝑡) 𝑢(T)mentioning

confidence: 99%

Section: 𝑥(𝑡 + 1) = 𝐴(𝑡) 𝑥(𝑡) + 𝐵(𝑡) 𝑢(T)mentioning

confidence: 99%

See 2 more Smart Citations

Dynamical Immunization of Data Network Controllability Processes Against Centrality Attacks on Temporal Networks

Arebi

2024

Preprint

Self Cite

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Resolution‐Dependent State Estimation for a Class of Nonlinear Coupled Complex Networks With Stochastic Communication and Correlated Noises

Chen,

Yue,

Jia

2024

Adaptive Control & Signal

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This article proposes the design of the resolution‐dependent variance‐constrained state estimation (RDVCSE) algorithm for a class of time‐varying nonlinear coupled complex networks (TVNCCNs) with stochastic communication and correlated noises. Specifically, a continuous‐differentiable nonlinear function with bounded first partial derivative is considered during the exchange among different coupled units and a resolution‐limited model is taken into account to embody the limited data‐processing capabilities of sensors. In order to describe the principle of random allocation in engineering, a stochastic strategy is employed in the sensor/estimator shared channel. An augmented RDVCSE method is developed such that the error covariance upper bound of state estimation (ECUBSE) can be guaranteed and obtained first. Then, the estimator parameter can be concretized via optimizing the trace of ECUBSE. In addition, a sufficient criterion is provided to verify the uniform boundedness of the presented RDVCSE algorithm. Finally, a comparative simulation is carried out to illustrate the validity of the introduced RDVCSE algorithm.

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A novel controllability method on complex temporal networks based on temporal motifs

Jin,

Arebi

2024

Concurrency and Computation

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SummaryComplex temporal networks have become instrumental in modeling dynamic systems across various disciplines, presenting unique challenges and opportunities in understanding and influencing their behavior. Controllability, a fundamental aspect of network dynamics, plays a pivotal role in manipulating these systems towards desired states. Temporal motifs are important patterns in temporal complex networks that have many applications in solving problems related to this type of networks. In this paper, a novel method for controlling temporal complex networks using temporal motifs is proposed. First, the most important effective temporal motifs in the controllability processes of complex networks have been identified and it has been shown that the network can be fully controlled using these temporal motifs. Then, an algorithm for extracting temporal motifs is proposed. This algorithm has been proposed to identify effective temporal motifs in network controllability to optimally identify control nodes. To increase the efficiency of extracting temporal motifs, a method for predicting the temporal motif‐based link has been proposed, which predicts temporal motifs. The results of the simulation of the proposed method based on temporal motifs and its implementation on real‐world temporal complex networks demonstrates that its performance was better than the conventional controllability methods.

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Event stream controllability on event-based complex networks

Cited by 28 publications

References 29 publications

Dynamical Immunization of Data Network Controllability Processes Against Centrality Attacks on Temporal Networks

Dynamical Immunization of Data Network Controllability Processes Against Centrality Attacks on Temporal Networks

Resolution‐Dependent State Estimation for a Class of Nonlinear Coupled Complex Networks With Stochastic Communication and Correlated Noises

A novel controllability method on complex temporal networks based on temporal motifs

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