Fast Connectivity Minimization on Large-Scale Networks

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.

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

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhen Wang ◽  
Mengting Jiang ◽  
Yu Yang ◽  
Lili Chen ◽  
Hong Ding
Keyword(s):  
Approximation Algorithm ◽  
Large Scale ◽  
Critical Infrastructure ◽  
Mixed Strategies ◽  
Strategy Space ◽  
Defense Strategies ◽  
Infrastructure Networks ◽  
The Game Theory ◽  
Large Scale Networks ◽  
Zero Sum

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.

Effective Service Composition in Large Scale Service Market

2012 ◽  
Vol 9 (1) ◽  
pp. 74-94 ◽  
Author(s):  
Xianzhi Wang ◽  
Zhongjie Wang ◽  
Xiaofei Xu
Keyword(s):  
Real Time ◽  
Service Composition ◽  
Large Scale ◽  
Effective Means ◽  
Optimal Solution ◽  
Solution Space ◽  
Coarse Grained ◽  
Service Market ◽  
Time Service ◽  
Special Cases

The web has undergone a tremendous shift from information repository to the provisioning capacity of services. As an effective means of constructing coarse-grained solutions by dynamically aggregating a set of services to satisfy complex requirements, traditional service composition suffers from dramatic decrease on the efficiency of determining the optimal solution when large scale services are available in the Internet based service market. Most current approaches look for the optimal composition solution by real-time computation, and the composition efficiency greatly depends on the adopted algorithms. To eliminate such deficiency, this paper proposes a semi-empirical composition approach which incorporates the extraction of empirical evidence from historical experiences to provide guidance to solution space reduction to real-time service selection. Service communities and historical requirements are further organized into clusters based on similarity measurement, and then the probabilistic correspondences between the two types of clusters are identified by statistical analysis. For each new request, its hosting requirement cluster would be identified and corresponding service clusters would be determined by leveraging Bayesian inference. Concrete services would be selected from the reduced solution space to constitute the final composition. Timing strategies for re-clustering and consideration to special cases in clustering ensures continual adaption of the approach to changing environment. Instead of relying solely on pure real-time computation, the approach distinguishes from traditional methods by combining the two perspectives together.

Optimal Consumption and Insurance: A Continuous-time Markov Chain Approach

2008 ◽  
Vol 38 (01) ◽  
pp. 231-257 ◽  
Author(s):  
Holger Kraft ◽  
Mogens Steffensen
Keyword(s):  
Continuous Time ◽  
Optimal Solution ◽  
Decision Problems ◽  
Optimal Consumption ◽  
Continuous Time Markov Chain ◽  
Financial Decision Making ◽  
Markov Chain Approach ◽  
Special Cases ◽  
Financial Decision ◽  
The Individual

Personal financial decision making plays an important role in modern finance. Decision problems about consumption and insurance are in this article modelled in a continuous-time multi-state Markovian framework. The optimal solution is derived and studied. The model, the problem, and its solution are exemplified by two special cases: In one model the individual takes optimal positions against the risk of dying; in another model the individual takes optimal positions against the risk of losing income as a consequence of disability or unemployment.

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

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.

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

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.

scholarly journals BFL: a node and edge betweenness based fast layout algorithm for large scale networks

2009 ◽  
Vol 10 (1) ◽  
pp. 19 ◽  
Author(s):  
Tatsunori B Hashimoto ◽  
Masao Nagasaki ◽  
Kaname Kojima ◽  
Satoru Miyano
Keyword(s):  
Large Scale ◽  
Layout Algorithm ◽  
Edge Betweenness ◽  
Large Scale Networks
Sign in / Sign up

Export Citation Format

Share Document