scholarly journals 2D Graph Plotter and Visualizer

2021 ◽  
pp. 01-04
Author(s):  
Akash Lalitkumar Makwana ◽  
Atik Zakirhusen Mujawar ◽  
Lalit Shailesh Jain ◽  
Bhargavi Dalal ◽  
Smita Bansod
Keyword(s):  
Social Networks ◽  
Line Graph ◽  
Linear Equations ◽  
Science And Engineering ◽  
Graph Plotter ◽  
Trigonometric Equations ◽  
Pie Chart ◽  
New Algorithms

In many fields of science and engineering, we often encounter the problem of solving N linear equations of type x + y and trigonometric equations. All technological, biological and social networks can be represented as graphs. Therefore, graphs are used in the research of new algorithms and protocols based on simulation in various fields of science. We aim to create a python-based graph generator that will draw any type of equation i.e., linear, algebraic, trigonometric and logarithmic on the graph. More importantly, the application is designed to draw multiple graphs on the same canvas and then analyse the results. We have included one more module where user can upload a CSV file consisting of raw and get a desired pie chart, line graph as well as bar graph. Our system mainly focuses on generating a output for a later analysis by downloading the graphs they have plotted.

scholarly journals Analys is and Creation of Free Sentiment Analysis Programs

2019 ◽  
Vol 25 (1) ◽  
pp. 83-105
Author(s):  
Josip Mihaljević
Keyword(s):  
Social Networks ◽  
Sentiment Analysis ◽  
Recurrent Neural Networks ◽  
Web Search ◽  
The Internet ◽  
Pilot Program ◽  
Online Programs ◽  
Input Text ◽  
Second Category ◽  
Pie Chart

This paper analyzes free online programs for sentiment analysis which can, on the bases of their algorithm, give a positive, negative or neutral opinion of a text. At the beginning of the paper sentiment analysis programs and techniques they use such as Naive Bayes and Recurrent Neural Networks are presented. The programs are divided into two categories for analysis. The fi rst category consists of sentiment analysis programs which analyze texts written or copied inside the user interface. The second category consists of programs for analyzing opinions posted on social networks, blogs, and other media sites. Programs from both categories were chosen for this research on the bases of positive reviews on computer science portals and their popularity on web search engin es such as Google and Bing. The accuracy of the programs from the fi rst category was checked by inserting the same sentence from movie reviews and comparing the results. Their additional options have also been analyzed. For the second category of programs, it was determined which social networks, blogs, and other social media they cover on the internet. The purpose of this analysis was to check the overall quality and options that free sentiment analysis programs provide. An example of how to create one’s own custom sentiment analyzer by using the available Python code and libraries found online is also given. Two simple programs were created using Python. The fi rst program belongs to the fi rst category of programs for analyzing an input text. This program serves as a pilot program for Croatian which gives only the basic analysis of sentences. The second program collects recent tweets from Twitter containing certain words and creates a pie chart based on the analysis of the results.

Improving the influence under IC-N model in social networks

2015 ◽  
Vol 07 (03) ◽  
pp. 1550037 ◽  
Author(s):  
Huan Ma ◽  
Yuqing Zhu ◽  
Deying Li ◽  
Donghyun Kim ◽  
Jun Liang
Keyword(s):  
Social Networks ◽  
Data Collection ◽  
Real World ◽  
Large Data ◽  
Maximization Problem ◽  
Influence Effect ◽  
Cascade Models ◽  
Total Influence ◽  
New Algorithms ◽  
Influence Maximization Problem

The influence maximization problem in social networks is to find a set of seed nodes such that the total influence effect is maximized under certain cascade models. In this paper, we propose a novel task of improving influence, which is to find strategies to allocate the investment budget under IC-N model. We prove that our influence improving problem is 𝒩𝒫-hard, and propose new algorithms under IC-N model. To the best of our knowledge, our work is the first one that studies influence improving problem under bounded budget when negative opinions emerge. Finally, we implement extensive experiments over a large data collection obtained from real-world social networks, and evaluate the performance of our approach.

New Algorithms for Secure Outsourcing of Large-Scale Systems of Linear Equations

2015 ◽  
Vol 10 (1) ◽  
pp. 69-78 ◽  
Author(s):  
Xiaofeng Chen ◽  
Xinyi Huang ◽  
Jin Li ◽  
Jianfeng Ma ◽  
Wenjing Lou ◽  
...  
Keyword(s):  
Large Scale ◽  
Linear Equations ◽  
Large Scale Systems ◽  
Secure Outsourcing ◽  
Systems Of Linear Equations ◽  
New Algorithms

FSIM: A Fast and Scalable Influence Maximization Algorithm Based on Community Detection

2018 ◽  
Vol 26 (03) ◽  
pp. 379-396 ◽  
Author(s):  
Esmaeil Bagheri ◽  
Gholamhossein Dastghaibyfard ◽  
Ali Hamzeh
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Influence Maximization ◽  
Experimental Results ◽  
Maximization Problem ◽  
Maximization Algorithms ◽  
New Algorithms ◽  
Influence Maximization Problem

Influence maximization algorithms try to select a set of individuals in social networks that are more influential. The Influence maximization problem is important in marketing and many researchers has researched on it and proposed new algorithms. All proposed algorithms are not scalable and are very time consuming for very large social networks generally. In this paper, a fast and scalable influence maximization algorithm called FSIM is proposed based on community detection. FSIM algorithm decreases number of nodes that must be examined without loss of the operations quality therefore it can find seeds quickly. FSIM can maximize influence in large social networks. Experimental results show FSIM is faster and more scalable than existing algorithms.

scholarly journals Mobile recommender systems: Identifying the major concepts

2018 ◽  
Vol 45 (3) ◽  
pp. 387-397 ◽  
Author(s):  
Elias Pimenidis ◽  
Nikolaos Polatidis ◽  
Haralambos Mouratidis
Keyword(s):  
Social Networks ◽  
Recommender Systems ◽  
Mobile Devices ◽  
User Experience ◽  
Rapid Growth ◽  
Information Overload ◽  
The Internet ◽  
Mobile Environments ◽  
Future Directions ◽  
New Algorithms

This article identifies the factors that have an impact on mobile recommender systems. Recommender systems have become a technology that has been widely used by various online applications in situations where there is an information overload problem. Numerous applications such as e-Commerce, video platforms and social networks provide personalised recommendations to their users and this has improved the user experience and vendor revenues. The development of recommender systems has been focused mostly on the proposal of new algorithms that provide more accurate recommendations. However, the use of mobile devices and the rapid growth of the Internet and networking infrastructure have brought the necessity of using mobile recommender systems. The links between web and mobile recommender systems are described along with how the recommendations in mobile environments can be improved. This work is focused on identifying the links between web and mobile recommender systems and to provide solid future directions that aim to lead in a more integrated mobile recommendation domain.

SCALES OF TIME AND THE ANTICIPATION OF THE FUTURE: GIBBON, SMITH, PLAYFAIR

2014 ◽  
Vol 11 (3) ◽  
pp. 697-718 ◽  
Author(s):  
JONATHAN SACHS
Keyword(s):  
Political Economy ◽  
Time Series ◽  
Business Cycle ◽  
Adam Smith ◽  
Line Graph ◽  
Temporal Scale ◽  
Economic Decline ◽  
The Future ◽  
Relative Decline ◽  
Pie Chart

This essay shows how Adam Smith addressed concerns about economic decline not only by proposing quantifiable categories through which relative decline could be measured, but also by characterizing the century as the proper timescale in which such quantities could be observed. What sometimes appears up close to be a process of decline and fall, Smith suggested, could, with a shift to a more distant long view, be explained instead as part of a normal business cycle. William Playfair then used Smith's emphasis on quantification to develop elaborate graphic techniques—what we now call the time-series line graph and the pie chart—to visualize more easily the patterns Smith sought to identify. Collectively, the reordering of temporal scale by Smith and Playfair helps us to rethink not only discourses of decline, but also our understanding of the temporalities of political economy as a problem of historical distance that needs to be thought about beyond temporal terms.

scholarly journals Determining Community Structure and Modularity in Social Network using Genetic Algorithm

2020 ◽  
Vol 14 (3) ◽  
pp. 219
Author(s):  
Taufan Bagus Dwi Putra Aditama ◽  
Azhari SN
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Community Structure ◽  
Social Network ◽  
Complex Networks ◽  
Bottlenose Dolphins ◽  
College Football ◽  
New Algorithms

 Research on determining community structure in complex networks has attracted a lot of attention in various applications, such as email networks and social networks. The popularity determines the structure of a community because it can analyze the structure.Meanwhile, to determine the structure of the community by maximizing the value of modularity is difficult. Therefore, a lot of research introduces new algorithms to solve problems in determining community structure and maximizing the value of modularity. Genetic Algorithm can provide effective solutions by combining exploration and exploitation.This study focuses on the Genetic Algorithm which added a cleanup feature in the process. The final results of this study are the results of a comparison of modularity values based on the determination of the community structure of the Genetic Algorithm, Girvan and Newman Algorithm, and the Louvain Algorithm. The best modularity values were obtained using the Genetic Algorithm which obtained 0.6833 results for Zachary's karate club dataset, 0.7446 for the Bottlenose dolphins dataset, 0.7242 for the American college football dataset, and 0.5892 for the Books about US politics dataset.

scholarly journals Eccentric pie charts and an unusual pie cutting

2020 ◽  
Vol 19 (4) ◽  
pp. 288-295
Author(s):  
Sándor Bozóki
Keyword(s):  
Polynomial System ◽  
Continuation Method ◽  
Arbitrary Point ◽  
Homotopy Continuation ◽  
Polynomial Functions ◽  
Homotopy Continuation Method ◽  
Trigonometric Equations ◽  
Coordinate Geometry ◽  
Pie Chart ◽  
Cutting Problem

The eccentric pie chart, a generalization of the traditional pie chart is introduced. An arbitrary point is fixed within the circle, and rays are drawn from it. A sector is bounded by a pair of neighboring rays and the arc between them. Eccentric pie charts have the potential of visualizing multiple sets of data, especially for small numbers of items/features. The calculations of the area-proportional diagram are based on well-known equations in coordinate geometry. The resulting system of polynomial and trigonometric equations can be approximated by a fully polynomial system, once the non-polynomial functions are approximated by their Taylor series written up to the first few terms. The roots of the polynomial system have been found by the homotopy continuation method, then used as starting points of a Newton iteration for the original (non-polynomial) system. The method is illustrated on a special pie-cutting problem.

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