Computing Effective Mixed Strategies for Protecting Targets in Large-Scale Critical Infrastructure Networks

Most critical infrastructure networks often suffer malicious attacks, which may result in network failures. Therefore, how to design more robust defense measures to minimize the loss is a great challenge. In recent years, defense strategies for enhancing the robustness of the networks are developed based on the game theory. However, the aforementioned method cannot effectively solve the defending problem on large-scale networks with a full strategy space. In this study, we achieve the purpose of protecting the infrastructure networks by allocating limited resources to monitor the targets. Based on the existing two-person zero-sum game model and the Double Oracle framework, we propose the EMSL algorithm which is an approximation algorithm based on a greedy search to compute effective mixed strategies for protecting large-scale networks. The improvement of our approximation algorithm to other algorithms is discussed. Experimental results show that our approximation algorithm can efficiently compute the mixed strategies on actual large-scale networks with a full strategy space, and the mixed defense strategies bring the highest utility to a defender on different networks when dealing with different attacks.

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Fast Connectivity Minimization on Large-Scale Networks

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3442342 ◽

2021 ◽

Vol 15 (3) ◽

pp. 1-25

Author(s):

Chen Chen ◽

Ruiyue Peng ◽

Lei Ying ◽

Hanghang Tong

Keyword(s):

Minimization Problem ◽

Computational Efficiency ◽

Large Scale ◽

Critical Infrastructure ◽

Optimal Solution ◽

Polynomial Algorithms ◽

Social Network Mining ◽

The Best Approximation ◽

Special Cases ◽

Large Scale Networks

The connectivity of networks has been widely studied in many high-impact applications, ranging from immunization, critical infrastructure analysis, social network mining, to bioinformatic system studies. Regardless of the end application domains, connectivity minimization has always been a fundamental task to effectively control the functioning of the underlying system. The combinatorial nature of the connectivity minimization problem imposes an exponential computational complexity to find the optimal solution, which is intractable in large systems. To tackle the computational barrier, greedy algorithm is extensively used to ensure a near-optimal solution by exploiting the diminishing returns property of the problem. Despite the empirical success, the theoretical and algorithmic challenges of the problems still remain wide open. On the theoretical side, the intrinsic hardness and the approximability of the general connectivity minimization problem are still unknown except for a few special cases. On the algorithmic side, existing algorithms are hard to balance between the optimization quality and computational efficiency. In this article, we address the two challenges by (1) proving that the general connectivity minimization problem is NP-hard and is the best approximation ratio for any polynomial algorithms, and (2) proposing the algorithm CONTAIN and its variant CONTAIN + that can well balance optimization effectiveness and computational efficiency for eigen-function based connectivity minimization problems in large networks.

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High Performance Start Node of Edge Ordering for Infrastructure Networks

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.721.693 ◽

2014 ◽

Vol 721 ◽

pp. 693-698

Author(s):

Bin Xie ◽

Xuan Liu ◽

Yu Chang Mo

Keyword(s):

Reliability Analysis ◽

High Performance ◽

Large Scale ◽

Network Reliability ◽

Distribution Patterns ◽

Infrastructure Networks ◽

Network Partitions ◽

Large Scale Networks ◽

Aviation Network ◽

Start Node

During the reliability analysis of infrastructure networks based on Binary Decision Diagram (BDD), we studied the high performance start node for edge ordering. We use node’s betweenness to divide the network into several partitions, and show the relation between high performance start nodes and network partitions. We summarized the distribution patterns of the high performance start nodes. The experiment results on selected aviation network shows that we can select high performance ordering start nodes for large-scale networks by using these patterns and network partitions. Thus, we can enhance the performance of network reliability analysis algorithm.

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Approximation Algorithm for Shortest Path in Large Social Networks

Algorithms ◽

10.3390/a13020036 ◽

2020 ◽

Vol 13 (2) ◽

pp. 36 ◽

Cited By ~ 1

Author(s):

Dennis Nii Ayeh Mensah ◽

Hui Gao ◽

Liang Wei Yang

Keyword(s):

Approximation Algorithm ◽

Large Scale ◽

High Efficiency ◽

Shortest Paths ◽

Real Life ◽

Original Network ◽

Hierarchical Networks ◽

Life Data ◽

Real Life Data ◽

Large Scale Networks

Proposed algorithms for calculating the shortest paths such as Dijikstra and Flowd-Warshall’s algorithms are limited to small networks due to computational complexity and cost. We propose an efficient and a more accurate approximation algorithm that is applicable to large scale networks. Our algorithm iteratively constructs levels of hierarchical networks by a node condensing procedure to construct hierarchical graphs until threshold. The shortest paths between nodes in the original network are approximated by considering their corresponding shortest paths in the highest hierarchy. Experiments on real life data show that our algorithm records high efficiency and accuracy compared with other algorithms.

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of Brain Cells & Circuits “Camillo Golgi”: The Neural Bases of Action – from cellular microcircuits to large-scale networks and modelling

School of Brain Cells & Circuits “Camillo Golgi”: The Neural Bases of Action – from cellular microcircuits to large-scale networks and modelling ◽

10.3389/978-2-88963-087-5 ◽

2019 ◽

Keyword(s):

Large Scale ◽

Brain Cells ◽

Neural Bases ◽

Large Scale Networks ◽

Camillo Golgi

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Contribution of Ionotropic Glutamatergic Receptors to Excitability and Attentional Signals in Macaque Frontal Eye Field

Cerebral Cortex ◽

10.1093/cercor/bhab007 ◽

2021 ◽

Author(s):

Miguel Dasilva ◽

Christian Brandt ◽

Marc Alwin Gieselmann ◽

Claudia Distler ◽

Alexander Thiele

Keyword(s):

Attentional Control ◽

Large Scale ◽

Neuronal Excitability ◽

Cell Types ◽

Receptor Activation ◽

Frontal Eye Field ◽

Control Signals ◽

Cognitive Operations ◽

Eye Field ◽

Large Scale Networks

Abstract Top-down attention, controlled by frontal cortical areas, is a key component of cognitive operations. How different neurotransmitters and neuromodulators flexibly change the cellular and network interactions with attention demands remains poorly understood. While acetylcholine and dopamine are critically involved, glutamatergic receptors have been proposed to play important roles. To understand their contribution to attentional signals, we investigated how ionotropic glutamatergic receptors in the frontal eye field (FEF) of male macaques contribute to neuronal excitability and attentional control signals in different cell types. Broad-spiking and narrow-spiking cells both required N-methyl-D-aspartic acid and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor activation for normal excitability, thereby affecting ongoing or stimulus-driven activity. However, attentional control signals were not dependent on either glutamatergic receptor type in broad- or narrow-spiking cells. A further subdivision of cell types into different functional types using cluster-analysis based on spike waveforms and spiking characteristics did not change the conclusions. This can be explained by a model where local blockade of specific ionotropic receptors is compensated by cell embedding in large-scale networks. It sets the glutamatergic system apart from the cholinergic system in FEF and demonstrates that a reduction in excitability is not sufficient to induce a reduction in attentional control signals.

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Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15 ◽

10.1145/2783258.2783385 ◽

2015 ◽

Cited By ~ 26

Author(s):

Michael Mitzenmacher ◽

Jakub Pachocki ◽

Richard Peng ◽

Charalampos Tsourakakis ◽

Shen Chen Xu

Keyword(s):

Large Scale ◽

Large Scale Networks

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How does the position of firms in the supply chain affect their performance? An empirical study

Applied Network Science ◽

10.1007/s41109-021-00364-9 ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

Siddharth Arora ◽

Alexandra Brintrup

Keyword(s):

Supply Chain ◽

Empirical Data ◽

Large Scale ◽

Individual Performance ◽

Supply Network ◽

Supply Chain Networks ◽

Focal Firm ◽

Cooperative Competition ◽

Large Scale Networks ◽

The Relationship

AbstractThe relationship between a firm and its supply chain has been well studied, however, the association between the position of firms in complex supply chain networks and their performance has not been adequately investigated. This is primarily due to insufficient availability of empirical data on large-scale networks. To addresses this gap in the literature, we investigate the relationship between embeddedness patterns of individual firms in a supply network and their performance using empirical data from the automotive industry. In this study, we devise three measures that characterize the embeddedness of individual firms in a supply network. These are namely: centrality, tier position, and triads. Our findings caution us that centrality impacts individual performance through a diminishing returns relationship. The second measure, tier position, allows us to investigate the concept of tiers in supply networks because we find that as networks emerge, the boundaries between tiers become unclear. Performance of suppliers degrade as they move away from the focal firm (i.e., Toyota). The final measure, triads, investigates the effect of buying and selling to firms that supply the same customer, portraying the level of competition and cooperation in a supplier’s network. We find that increased coopetition (i.e., cooperative competition) is a performance enhancer, however, excessive complexity resulting from being involved in both upstream and downstream coopetition results in diminishing performance. These original insights help understand the drivers of firm performance from a network perspective and provide a basis for further research.

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