scholarly journals CLUSTERING METHODS IN LARGE-SCALE SYSTEMS

2020 ◽  
Vol 6 (5) ◽  
pp. 21-24
Author(s):  
Denis V. Gadasin ◽  
◽  
Andrey V. Shvedov ◽  
Alyona A. Yudin ◽  
◽  
...  
Keyword(s):  
Social Network ◽  
Complex Networks ◽  
Large Scale ◽  
Physical Nature ◽  
Clustering Methods ◽  
Large Scale Systems ◽  
Practical Applications ◽  
System Properties ◽  
Definition Of ◽  
And Training

Interactions between people, groups, organizations, and biological cells have a relationship character that can be represented as a network. The system properties of such networks, regardless of their physical nature, but clearly determining the performance of networks, create the totality of the real world. Complex networks – are naturally existing networks (graphs) that have complex topological properties. The researchers who participate and also make discoveries in this field come from various Sciences such as mathematics, computer science, physics, sociology, and engineering. Therefore, the results of research carry both theoretical knowledge and practical applications in these Sciences. This paper discusses the definition of complex networks. The main characteristics of complex networks, such as clustering and congestion, are considered. A popular social network is considered as a complex network. The calculation of nodes and links of the considered social network is made. The main types of AI development and training are highlighted.

Social Computing

2018 ◽  
pp. 7796-7804
Author(s):  
Nolan Hemmatazad
Keyword(s):  
Large Scale ◽  
Social Computing ◽  
Social Needs ◽  
Content Creation ◽  
Broad Definition ◽  
Practical Applications ◽  
Research Themes ◽  
The Social ◽  
Definition Of ◽  
Near Future

Broadly speaking, social computing encapsulates the idea of making technologies more aware of, and more in alignment with, the social needs of their users. This allows for the introduction of new modes of communication and collaboration among users, the ability to establish and grow communities of various constitutions, as well as for more dynamic and large scale content creation, dissemination, and evaluation. This chapter serves the ternary purpose of establishing a broad definition of social computing as it stands today and as it is expected to evolve in the near future, providing an overview of the practical applications of social computing, and examining the present and historic research themes that have made an impression on social computing as an area of academic intrigue. The chapter is intended to be accessible to casual readers, practitioners, and academicians alike, with little technical depth and broad focus throughout, for the purpose of establishing an initial acquaintance with the field.

Social Computing

2019 ◽  
pp. 1200-1210
Author(s):  
Nolan Hemmatazad
Keyword(s):  
Large Scale ◽  
Social Computing ◽  
Social Needs ◽  
Content Creation ◽  
Broad Definition ◽  
Practical Applications ◽  
Research Themes ◽  
The Social ◽  
Definition Of ◽  
Near Future

Broadly speaking, social computing encapsulates the idea of making technologies more aware of, and more in alignment with, the social needs of their users. This allows for the introduction of new modes of communication and collaboration among users, the ability to establish and grow communities of various constitutions, as well as for more dynamic and large-scale content creation, dissemination, and evaluation. This chapter serves the ternary purpose of establishing a broad definition of social computing as it stands today and as it is expected to evolve in the near future, providing an overview of the practical applications of social computing, and examining the present and historic research themes that have made an impression on social computing as an area of academic intrigue. The chapter is intended to be accessible to casual readers, practitioners, and academicians alike, with little technical depth and broad focus throughout, for the purpose of establishing an initial acquaintance with the field.

scholarly journals A Method To Improve The Time Of Computing For Detecting Community Structure In Social Network Graph

2019 ◽  
Vol 8 (6) ◽  
pp. 933-937
Keyword(s):  
Community Structure ◽  
Social Network ◽  
Complex Networks ◽  
Large Scale ◽  
Linear Time ◽  
A Priori ◽  
Label Propagation ◽  
Data Sets ◽  
Network Graph ◽  
Priori Information

Identifying communities has always been a fundamental task in analysis of complex networks. Currently used algorithms that identify the community structures in large-scale real-world networks require a priori information such as the number and sizes of communities or are computationally expensive. Amongst them, the label propagation algorithm (LPA) brings great scaslability together with high accuracy but which is not accurate enough because of its randomness. In this paper, we study the equivalence properties of nodes on social network graphs according to the labeling criteria to shorten social network graphs and develop label propagation algorithms on shortened graphs to discover effective social networking communities without requiring optimization of the objective function as well as advanced information about communities. Test results on sample data sets show that the proposed algorithm execution time is significantly reduced compared to the published algorithms. The proposed algorithm takes an almost linear time and improves the overall quality of the identified community in complex networks with a clear community structure.

Reduction of linear dynamic systems using generalized approach of pole clustering method

2021 ◽  
pp. 014233122110633
Author(s):  
Arvind Kumar Prajapati ◽  
Rajendra Prasad
Keyword(s):  
Large Scale ◽  
Original System ◽  
Clustering Methods ◽  
Clustering Method ◽  
Large Scale Systems ◽  
Linear Dynamic Systems ◽  
Clustering Technique ◽  
Cluster Centre ◽  
Performance Error ◽  
Poles And Zeros

A new model order abatement method based on the clustering of poles and zeros of a large-scale continuous time system is proposed. The clustering of poles and zeros are used for finding the cluster centres. The abated model is identified from the cluster centres, which reflect the effectiveness of the dominant poles of the clusters. The cluster centre is determined by taking [Formula: see text] root of the sum of the inverse of [Formula: see text] power of poles (zeros) in a particular cluster. It is famous that the magnitude of the pole cluster centre plays an important role in the clustering technique for the simplification of large-scale systems. The magnitude of the cluster centres computed by the modified pole clustering method or some other methods based on the pole clustering techniques is large as compared to the proposed technique. The less magnitude of pole cluster centre reflects the better approximations and proper matching of the abated model with the original system. Therefore, the proposed method offers better approximations matching between actual and abated systems during the transient period compared to some other clustering methods, which supports the replacement of large-scale systems by proposed abated systems. The proposed technique is a generalized version of the standard pole clustering technique. The proposed method guarantees the retention of dominant poles, stability and other fundamental control properties of the actual plant in the abated model. The proposed algorithm is illustrated by the five standard systems taken from the literature. The accuracy and effectiveness of the proposed method are verified by comparing the time responses and various performance error indices.

Entity Resolution on Complex Network

2014 ◽  
pp. 195-221
Keyword(s):  
Social Network ◽  
Complex Networks ◽  
Complex Network ◽  
Binary Relation ◽  
Entity Resolution ◽  
The Internet ◽  
Information Searching ◽  
Practical Applications ◽  
Structure Information

Complex networks can be used to describe the Internet, social network, or more broadly describe a binary relation of a set of objects. Structure information of complex network helps the identification of the entity corresponding to nodes in the network. There is much research in this area, and the authors introduce these studies and their results in this chapter. The authors mainly present two practical applications as an example. Through these examples, the authors explore the research ideas in entity resolution on complex network. The applications of entity resolution on complex network include the detection of mirror Websites, name recognition in social network, and information searching on the Internet. This chapter introduces some applications, including the detection of mirror Websites and name recognition, in social network in detail.

scholarly journals Regular Equivalence for Social Networks

2018 ◽  
Vol 9 (1) ◽  
pp. 117 ◽  
Author(s):  
Pieter Audenaert ◽  
Didier Colle ◽  
Mario Pickavet
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Efficient Algorithm ◽  
Large Scale ◽  
Real Life ◽  
Equivalence Classes ◽  
Random Networks ◽  
Formal Definition ◽  
Regular Equivalence ◽  
Definition Of

Networks and graphs are highly relevant in modeling real-life communities and their interactions. In order to gain insight in their structure, different roles are attributed to vertices, effectively clustering them in equivalence classes. A new formal definition of regular equivalence is presented in this paper, and the relation with other equivalence types is investigated and mathematically proven. An efficient algorithm is designed, able to detect all regularly equivalent roles in large-scale complex networks. We apply it to both Barabási–Albert random networks, as well as real-life social networks, which leads to interesting insights.

Resolution and measurement in the scanning electron microscope

1987 ◽  
Vol 45 ◽  
pp. 534-537 ◽  
Author(s):  
Michael T. Postek
Keyword(s):  
Electron Microscope ◽  
Scanning Electron Microscope ◽  
Engineering Design ◽  
Resolving Power ◽  
Practical Applications ◽  
Instrument Resolution ◽  
Accurate Definition ◽  
Definition Of ◽  
Scanning Electron ◽  
Better Than

The term ultimate resolution or resolving power is the very best performance that can be obtained from a scanning electron microscope (SEM) given the optimum instrumental conditions and sample. However, as it relates to SEM users, the conventional definitions of this figure are ambiguous. The numbers quoted for the resolution of an instrument are not only theoretically derived, but are also verified through the direct measurement of images on micrographs. However, the samples commonly used for this purpose are specifically optimized for the measurement of instrument resolution and are most often not typical of the sample used in practical applications.SEM RESOLUTION. Some instruments resolve better than others either due to engineering design or other reasons. There is no definitively accurate definition of how to quantify instrument resolution and its measurement in the SEM.

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