scholarly journals Tree Sampling for Detection of Information Source in Densely Connected Networks

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 587
Author(s):  
Taewon Min ◽  
Changhee Joo
Keyword(s):  
Autonomous System ◽  
Large Scale ◽  
Information Source ◽  
Low Complexity ◽  
Infection Process ◽  
The Internet ◽  
Source Detection ◽  
Detection Problem ◽  
Tree Networks ◽  
Large Scale Networks

We investigate the problem of source detection in information spreading throughout a densely-connected network. Previous works have been developed mostly for tree networks or applied the tree-network results to non-tree networks assuming that the infection occurs in the breadth first manner. However, these approaches result in low detection performance in densely-connected networks, since there is a substantial number of nodes that are infected through the non-shortest path. In this work, we take a two-step approach to the source detection problem in densely-connected networks. By introducing the concept of detour nodes, we first sample trees that the infection process likely follows and effectively compare the probability of the sampled trees. Our solution has low complexity of O ( n 2 log n ) , where n denotes the number of infected nodes, and thus can be applied to large-scale networks. Through extensive simulations including practical networks of the Internet autonomous system and power grid, we evaluate our solution in comparison with two well-known previous schemes and show that it achieves the best performance in densely-connected networks.

scholarly journals Computation of the Double Metric Dimension in Convex Polytopes

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Liying Pan ◽  
Muhammad Ahmad ◽  
Zohaib Zahid ◽  
Sohail Zafar
Keyword(s):  
Source Localization ◽  
Computer Network ◽  
Convex Polytopes ◽  
The Internet ◽  
Metric Dimension ◽  
Human Beings ◽  
Source Detection ◽  
Resolving Set ◽  
Detection Problem ◽  
Rumor Spreading

A source detection problem in complex networks has been studied widely. Source localization has much importance in order to model many real-world phenomena, for instance, spreading of a virus in a computer network, epidemics in human beings, and rumor spreading on the internet. A source localization problem is to identify a node in the network that gives the best description of the observed diffusion. For this purpose, we select a subset of nodes with least size such that the source can be uniquely located. This is equivalent to find the minimal doubly resolving set of a network. In this article, we have computed the double metric dimension of convex polytopes R n and Q n by describing their minimal doubly resolving sets.

scholarly journals Intelligent Algorithms of Wireless Communication for Internet of Things Service Security and Privacy Assurance

2021 ◽  
Author(s):  
Yuanguo Wang ◽  
Xiaogang Jiang ◽  
Qian Yu ◽  
Xiuling Zhang ◽  
Bailu Zhao ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Communication Networks ◽  
Data Exchange ◽  
Large Scale ◽  
Low Complexity ◽  
Internet Security ◽  
Security And Privacy ◽  
The Internet ◽  
Management Service ◽  
The Internet Of Things

Abstract Due to its huge application potential, the Internet of Things has received extensive attention from governments, academia and industry. The core concepts of the Internet of Things are perception, control, transmission and intelligence. Through technical means, the coordination of things and things, people and things, and people and people has been realized, thus forming a network based on sensor networks, the Internet, and mobile communication networks. A larger complex network system. However, restricted by the characteristics of network structure, terminal equipment, communication methods, application scenarios, etc., some security and privacy issues unique to the Internet of Things cannot be directly solved by existing Internet security technologies. Aiming at the general high complexity of existing algorithms, this article starts with the different phase-frequency characteristics of different filters, and designs a new low-complexity reduction system algorithm. According to the characteristics of the system that the filter structure can be flexibly selected, the method randomly allocates different filters to each sub-carrier and adjusts the phase of signal superimposition, thereby constructing a coordinated communication facility and management service coordination suitable for large-scale distributed IoT services. The interactive access control architecture realizes the confidentiality of data exchange between services.

Studying Information Infrastructures

2012 ◽  
pp. 143-158 ◽  
Author(s):  
Petter Nielsen
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
The Internet ◽  
Steady Increase ◽  
National Information Infrastructure ◽  
Shared Resource ◽  
Long Period ◽  
Information Infrastructures ◽  
Large Scale Networks ◽  
National Information

As a result of a steady increase in reach, range, and processing capabilities, information systems no longer appear as independent, but rather as integrated, parts of large scale networks. These networks offer a shared resource for information delivery and exchange to communities, which appropriate them for their respective purposes. Such information infrastructures are complex in several ways. As they are composed of a variety of different components, their openness and heterogeneity make them inherently uncontrollable; through their expansion, these various interconnected networks enter new interdependencies; while they are based on extending existing technical and social networks, they also need to develop and grow over a long period of time; and, they are developed as a distributed activity. Examples of such information infrastructures include the Internet, National Information Infrastructure (NII) initiatives and industry-wide EDI networks, as well as corporate-wide implementations of enterprise systems.

scholarly journals Accelerating the Internet in the presence of Big Data: Reducing user delays by leveraging historical user request patterns for web caching

2019 ◽  
Vol 16 ◽  
pp. 1-10
Author(s):  
Chetan Kumar ◽  
Sean Marston
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Web Caching ◽  
The Internet ◽  
Proxy Caching ◽  
Data Usage ◽  
User Request ◽  
Large Scale Networks ◽  
Caching Mechanism ◽  
Significant Intensification

Approximately 4 billion people have access to the Internet, additionally 23 billion devices are connected as of 2018. This has allowed for a substantial growth in data collection which has allowed for Big Data to flourish. The continued increase in user, devices, and Big Data usage has created a significant intensification in Internet traffic. This in turn has the potential to increase user delays when accessing data on the Internet. There are a number of ways to help reduce user latency, web caching is able to reduce web user delays in addition to reducing network traffic and the load on web servers. In this study we propose a proxy level web caching mechanism leveraging historical web patterns to help reduce user latency and accelerate the Internet. In addition we survey the state of the art of other caching approaches. Our investigation shows that using historical patterns as part of a proxy caching mechanisms in large scale networks can significantly shorten the latency for users in this era of Big Data

scholarly journals Multi-Objective Evolutionary Algorithms to Find Community Structures in Large Networks

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2048 ◽  
Author(s):  
Manuel Guerrero ◽  
Consolación Gil ◽  
Francisco G. Montoya ◽  
Alfredo Alcayde ◽  
Raúl Baños
Keyword(s):  
Evolutionary Algorithms ◽  
Power Systems ◽  
Community Detection ◽  
Large Scale ◽  
Real Life ◽  
Resolution Limit ◽  
Detection Problem ◽  
Multi Objective ◽  
Large Scale Networks ◽  
Structural Metrics

Real-world complex systems are often modeled by networks such that the elements are represented by vertices and their interactions are represented by edges. An important characteristic of these networks is that they contain clusters of vertices densely linked amongst themselves and more sparsely connected to nodes outside the cluster. Community detection in networks has become an emerging area of investigation in recent years, but most papers aim to solve single-objective formulations, often focused on optimizing structural metrics, including the modularity measure. However, several studies have highlighted that considering modularityas a unique objective often involves resolution limit and imbalance inconveniences. This paper opens a new avenue of research in the study of multi-objective variants of the classical community detection problem by applying multi-objective evolutionary algorithms that simultaneously optimize different objectives. In particular, they analyzed two multi-objective variants involving not only modularity but also the conductance metric and the imbalance in the number of nodes of the communities. With this aim, a new Pareto-based multi-objective evolutionary algorithm is presented that includes advanced initialization strategies and search operators. The results obtained when solving large-scale networks representing real-life power systems show the good performance of these methods and demonstrate that it is possible to obtain a balanced number of nodes in the clusters formed while also having high modularity and conductance values.

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.

Sign in / Sign up

Export Citation Format

Share Document