Applied Modeling Techniques and Data Analysis 1

Computational Data Analysis Methods and Tools

Yiannis Dimotikalis, Alex Karagrigoriou, Christina Parpoula, Christos H. Skiadas

• Publisher: Wiley-ISTE
• Year: 2021
• ISBN:
  9781119821564
• Edition: 1
• Topic: Business
• Subtopic: Management
• Index: Business

BIG DATA, ARTIFICIAL INTELLIGENCE AND DATA ANALYSIS SET Coordinated by Jacques Janssen

Data analysis is a scientific field that continues to grow enormously, most notably over the last few decades, following rapid growth within the tech industry, as well as the wide applicability of computational techniques alongside new advances in analytic tools. Modeling enables data analysts to identify relationships, make predictions, and to understand, interpret and visualize the extracted information more strategically.

This book includes the most recent advances on this topic, meeting increasing demand from wide circles of the scientific community. Applied Modeling Techniques and Data Analysis 1 is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians, working on the front end of data analysis and modeling applications. The chapters cover a cross section of current concerns and research interests in the above scientific areas. The collected material is divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications.

PART 1
Computational Data Analysis

1
A Variant of Updating PageRank in Evolving Tree Graphs

A PageRank update refers to the process of computing new PageRank values after a change(s) (addition or removal of links/vertices) has occurred in real-life networks. The purpose of updating is to avoid re-calculating the values from scratch. To efficiently carry out the update, we consider PageRank to be the expected number of visits to a target vertex if multiple random walks are performed, starting at each vertex once and weighing each of these walks by a weight value. Hence, it might be looked at as updating a non-normalized PageRank. We focus on networks of tree graphs and propose an approach to sequentially update a scaled adjacency matrix after every change, as well as the levels of the vertices. In this way, we can update the PageRank of affected vertices by their corresponding levels.

1.1. Introduction

Most real-world networks are continuously changing, and this phenomenon has come with challenges to the known data mining algorithms that assume the static form of datasets (Bahmani et al. 2012). Besides, it is a primary aim of network analysts to keep track of critical nodes.

Since Brin and Page (1998) pioneered PageRank centrality more than two decades ago, the centrality measure has found its applications in many disciplines (Gleich 2015). Recently, the study of PageRank of evolving graphs has drawn considerable attention and several computational models have been proposed. For instance, Langville and Meyer (2006) proposed an algorithm to update PageRank using an aggregation/disaggregation concept. In Bahmani et al. (2012), an algorithm that estimates a PageRank vector by crawling a small portion of the graph has been proposed, while Ohsaka et al. (2015) proposed an updating method for personalized PageRanks. In addition, the idea of partitioning information networks into connected acyclic and strongly connected components and then applying iterative methods of solving linear systems – while keeping an eye on the component(s) that has evolved – is proposed in Engström (2016) and Engström and Silvestrov (2016). In fact, all of these authors treat ...

Table of contents

• Cover
• Table of Contents
• Title Page
• Copyright
• Preface
• PART 1: Computational Data Analysis
• PART 2: Data Analysis Methods and Tools
• List of Authors
• Index
• End User License Agreement