scholarly journals A Binary Search Algorithm for Correlation Study of Decay Centrality vs. Degree Centrality and Closeness Centrality

2017 ◽  
Vol 10 (2) ◽  
pp. 52
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Real World ◽  
Search Algorithm ◽  
Threshold Value ◽  
Search Space ◽  
Correlation Study ◽  
Binary Search ◽  
Closeness Centrality ◽  
Node Degree ◽  
Degree Centrality ◽  
Binary Search Algorithm

Results of correlation study (using Pearson's correlation coefficient, PCC) between decay centrality (DEC) vs. degree centrality (DEG) and closeness centrality (CLC) for a suite of 48 real-world networks indicate an interesting trend: PCC(DEC, DEG) decreases with increase in the decay parameter δ (0 < δ < 1) and PCC(DEC, CLC) decreases with decrease in δ. We make use of this trend of monotonic decrease in the PCC values (from both sides of the δ-search space) and propose a binary search algorithm that (given a threshold value r for the PCC) could be used to identify a value of δ (if one exists, we say there exists a positive δ-spacer) for a real-world network such that PCC(DEC, DEG) ≥ r as well as PCC(DEC, CLC) ≥ r. We show the use of the binary search algorithm to find the maximum Threshold PCC value rmax (such that δ-spacermax is positive) for a real-world network. We observe a very strong correlation between rmax and PCC(DEG, CLC) as well as observe real-world networks with a larger variation in node degree to more likely have a lower rmax value and vice-versa.

Clique Size and Centrality Metrics for Analysis of Real-World Network Graphs

2019 ◽  
pp. 1183-1198
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Real World ◽  
Random Network ◽  
Maximal Clique ◽  
Closeness Centrality ◽  
Node Degree ◽  
Degree Centrality ◽  
Eigenvector Centrality ◽  
Scale Free ◽  
Centrality Metrics ◽  
Free Network

The authors present correlation analysis between the centrality values observed for nodes (a computationally lightweight metric) and the maximal clique size (a computationally hard metric) that each node is part of in complex real-world network graphs. They consider the four common centrality metrics: degree centrality (DegC), eigenvector centrality (EVC), closeness centrality (ClC), and betweenness centrality (BWC). They define the maximal clique size for a node as the size of the largest clique (in terms of the number of constituent nodes) the node is part of. The real-world network graphs studied range from regular random network graphs to scale-free network graphs. The authors observe that the correlation between the centrality value and the maximal clique size for a node increases with increase in the spectral radius ratio for node degree, which is a measure of the variation of the node degree in the network. They observe the degree-based centrality metrics (DegC and EVC) to be relatively better correlated with the maximal clique size compared to the shortest path-based centrality metrics (ClC and BWC).

Clique Size and Centrality Metrics for Analysis of Real-World Network Graphs

2018 ◽  
pp. 6507-6521
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Real World ◽  
Random Network ◽  
Maximal Clique ◽  
Closeness Centrality ◽  
Node Degree ◽  
Degree Centrality ◽  
Eigenvector Centrality ◽  
Scale Free ◽  
Centrality Metrics ◽  
Free Network

We present correlation analysis between the centrality values observed for nodes (a computationally lightweight metric) and the maximal clique size (a computationally hard metric) that each node is part of in complex real-world network graphs. We consider the four common centrality metrics: degree centrality (DegC), eigenvector centrality (EVC), closeness centrality (ClC) and betweenness centrality (BWC). We define the maximal clique size for a node as the size of the largest clique (in terms of the number of constituent nodes) the node is part of. The real-world network graphs studied range from regular random network graphs to scale-free network graphs. We observe that the correlation between the centrality value and the maximal clique size for a node increases with increase in the spectral radius ratio for node degree, which is a measure of the variation of the node degree in the network. We observe the degree-based centrality metrics (DegC and EVC) to be relatively better correlated with the maximal clique size compared to the shortest path-based centrality metrics (ClC and BWC).

scholarly journals Aplikasi belajar tajwid menggunakan binary search

JURNAL ELTEK ◽  
2021 ◽  
Vol 19 (2) ◽  
pp. 1
Author(s):  
Anita Andriani ◽  
Arbiati Faizah ◽  
Indana Lazulfa ◽  
Istiqomah Istiqomah
Keyword(s):  
Digital Technology ◽  
Search Algorithm ◽  
Search Space ◽  
Binary Search ◽  
Early Age ◽  
Motivation To Learn ◽  
Instruction Manual ◽  
Binary Search Algorithm ◽  
The Law

ABSTRAK Tajwid dapat diartikan sebagai aturan atau tata cara untuk membaca Al-Quran dengan benar. seorang muslim biasanya mulai belajar tajwid sejak usianya masih dini, namun dewasa ini motivasi untuk mempelajarinya makin turun. Salah satu penyebabnya adalah cara pembelajaran yang konvensional dan kurang begitu memanfaatkan teknologi. Oleh karena itu, penelitian ini bertujuan untuk membangun aplikasi pembelajaran tajwid berbasis website dan menerapkan algoritma binary search untuk memperoleh hasil yang lebih optimal. Pencarian biner atau binary search adalah suatu algoritma yang bekerja dengan mencari posisi elemen dalam array yang telah diurutkan sebelumnya dan kemudian membandingkan nilainya. Ruang pencarian dibagi menjadi dua, kemudian nilai yang dicari dibandingkan dengan elemen tengah array. Operasi ini diulang terus sampai ada kecocokan diantara dua nilai tersebut. Aplikasi pembelajaran tajwid yang dibuat terdiri dari tiga menu yaitu materi, latihan, dan info. Menu materi berisi mata kuliah yang berkaitan dengan hukum membaca Al-Quran. Menu latihan memungkinkan pengguna untuk melatih pemahaman yang telah mereka pelajari di menu teori, sedangkan menu info berisi instruksi manual penggunaan aplikasi. ABSTRACT Tajweed is set of rules for reciting Al-Quran properly. Muslim start learning tajweed from their early age, but currently motivation to learn tajweed is decreasing. One of the reason is the conventional learning and less use digital technology. Therefore, the aim of this study is to construct tajweed learning application based on website and applied binary search algorithm for better result. Binary search is a search algorithm that works by finding the position of elements in a sorted array and comparing their values. The search space is divided into half, then compared the value searched with the middle element of the Array.  This operation is repeated until they match. Tajweed learning application consist of three menus: theory, exercise, and info. Theory menu contains courses related to the law of reciting Al-Quran. Exercise menu allows user to train the understanding they has learned in theory menu, while info menu contains user instruction manual.

scholarly journals Practical Implementation of an Indoor Robot Localization and Identification System using an Array of Anchor Nodes

2020 ◽  
Vol 16 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Israa AL-Forati ◽  
Abdulmuttalib Rashid
Keyword(s):  
Search Algorithm ◽  
Low Cost ◽  
Binary Search ◽  
Practical Implementation ◽  
Identification System ◽  
Robot Localization ◽  
Light Emitting ◽  
The Matrix ◽  
Anchor Nodes ◽  
Binary Search Algorithm

This paper proposes a low-cost Light Emitting Diodes (LED) system with a novel arrangement that allows an indoor multi-robot localization. The proposed system uses only a matrix of low-cost LED installed uniformly on the ground of an environment and low-cost Light Dependent Resistor (LDR), each equipped on bottom of the robot for detection. The matrix of LEDs which are driven by a modified binary search algorithm are used as active beacons. The robot localizes itself based on the signals it receives from a group of neighbor LEDs. The minimum bounded circle algorithm is used to draw a virtual circle from the information collected from the neighbor LEDs and the center of this circle represents the robot’s location. The propose system is practically implemented on an environment with (16*16) matrix of LEDs. The experimental results show good performance in the localization process.

Routing Lookup Algorithm Performance Analysis and Research

2011 ◽  
Vol 181-182 ◽  
pp. 109-112
Author(s):  
Xian Min Wei
Keyword(s):  
Performance Analysis ◽  
Search Algorithm ◽  
Binary Search ◽  
Algorithm Performance ◽  
Practical Performance ◽  
Binary Search Algorithm ◽  
Routing Lookup ◽  
Searching Performance

This paper describes several current routing lookup algorithms, to study and analysize the complexity and operating practical performance of these routing lookup algorithms. The results show that although the binary search algorithm is not greatly improved in the searching performance, but in IPv6 environment, because searching performance of multi-branch Trie tree will decrease greatly, thus the superiority of binary search algorithm will be reflected better.

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