COMPLEX NETWORK MODELLING AND ANALYSIS

OF HAJJ CROWD AND MERS-COV OUTBREAK

BY

AKHLAQ AHMAD

A thesis submitted in fulfilment of the requirement for the

degree of Doctor of Philosophy in Computer Science

Kulliyyah of Information and Communication Technology

International Islamic University Malaysia

NOVEMBER 2017

ii

ABSTRACT

Representing, analysing and modelling natural and artificially-existing systems into

complex networks provides a useful insight into understanding the behaviour of these

networks in detail and predicting their future. Such networks share the common

property that their vertex connectivity follows a scale-free behaviour due to their

evolving nature, and new node addition take place under preferential attachment

behaviour. Hubs or highly connected nodes are widely believed to have special

importance in network management. An attack on these hubs can cause a serious and

irreparable damage to the functionality of the whole network. The resilience of these

targeted nodes, and therefore the entire network, cannot be achieved without effectively

tracking them within the network. To improve services offered to a large network of

people who have gathered for a particular purpose, modelling and analysis of the

individual and their collective behaviour is very important. Hajj is an annual religious

gathering, organized for a duration of about a week, where millions of pilgrims from all

over the world get together to perform religious rituals within certain Spatio-temporal

zones. The Hajj management faces many challenges, including cultural diversity,

literacy level, religious knowledge about rituals, lack of availability of translators and

shortage of time available at hand in emergency situations. To study Hajj Crowd as a

complex network, we have deployed a crowdsourcing framework that uses

smartphones’ sensory data to capture spatial and temporal coordinates, with the ability

to define quality aware user contexts. To deal with the Spatio-temporal and activity data

from the crowd, we have developed a cloud-based framework that can receive and store

context and user interaction data from the smartphone application. We have analysed

the data, defined a high-quality user context, and provided context-aware services as an

incentive. We have modelled Hajj Crowd as Hajj Geo Social Network (HGSN), a one-

mode complex network of pilgrims that uses commonly available communication

services to help them perform their Spatio-temporal religious activities. Initially, we

have analysed the HGSN with respect to different metrics, such as, node degree,

betweenness, closeness, average path length. It was observed that HGSN is an evolving

network and the node-degree follows the power law behaviour. Moreover, we have

discovered that pilgrims with many acquaintances or those who are members of

linguistically diverse subnets can bridge these subnets and can play a key role in

information diffusion. The Recent outbreak of MERS-CoV (Middle East Respiratory

Syndrome-Corona Virus) has raised a serious threat to pilgrims’ health. This is a global

concern that this infectious disease can result in a worldwide outbreak once visitors

return to their home countries, resulting in the creation of a disease spread network

spanning all affected areas, with its hub centered at the location of the Hajj event. We

have utilized the spatio-temporal information available about MERS-CoV reported

cases in Saudi Arabia and modelled it as MERS-CoV network between different

location where MERS-CoV cases were reported. The same network analysis approach

was applied to study the impact of MERS-CoV spreading on Hajj related areas.

Analyzing this network can help concerned authorities deduce useful information to

combat the MERS-CoV spreading regarding areas of concerns to pilgrim health.

iii

البحث خلاصة

البيانات المكانية والزمانية لحشود كبيرة باعتباره شبكة غير معتمدة الحجمتمثيل وتحليل ونمذجة النظم الطبيعية والمصطنعة الموجودة في شبكات معقدة توفر معلومات مفيدة لفهم

وك مكوناتها والتنبؤ بمستقبلهم. الشبكات العصبية، تنتمي إلى مجالات مختلفة ولكنها تشترك في خاصية سلوهي أن الترابط فيهم يخضع لقانون الأس الغير معتمد على الحجم. فعندما يضاف نقطة إلى هذه الشبكة

كشبكة الناس في تجمع يزداد احتمال اتصال هذه النقطة إلى نقطة متصلة أكثر. في الشبكات الطبيعيةكبير، النمذجة والتحليل مهمان دائما لفهم سلوك الفردي والجماعي لتحسين الترتيبات المتاحة. الحج هو تجمع ديني سنوي لمدة حوالي أسبوع، حيث الملايين من الحجاج من جميع أنحاء العالم يجتمعون لأداء

إدارة الحج العديد من التحديات من جهة الحجاج، بما في المناسك داخل المناطق المكانية والزمانية. وتواجه ذلك التنوع الثقافي وخلفيات مختلفة، ومعدل الإلمام بالقراءة والكتابة، والمعرفة الدينية حول المناسك، وعدم توافر مترجمين لجميع اللغات وضيق الوقت المتاح في متناول اليد في حالات الطوارئ. لدراسة حشد الحجاج

معقدة، قمنا بنشر إطار التعهيد الجماعي التي تستخدم الحساسات فى الهواتف الذكية لالتقاط كشبكةالإحداثيات المكانية والزمانية وتطوير الإطار على السحابة التي يمكنها تلقي بيانات السياق وتخزينها عن

بأعلى جودة، ونقدم طريق تطبيق الهاتف الذكي. نحن نقوم بتحليل البيانات، وتحديد سياق المستخدمخدمات واعية للسياق كحافز. لقد قمنا بنمذجة حشود الحج كشبكة معقدة موحدة النمط بين حجاج بيت الله الحرام من خلال خدمات الاتصالات المقدمة لمساعدتهم أثناء أداء أنشطتهم المكانية والزمانية. في

ولوحظ أن الشبكة تتطور، وتعقيد الدرجة يتبع البداية، لقد طبقنا مقاييس الشبكات لتحليل هذه الشبكة.سلوك قانون القوة، لذلك فإن الشبكة غير معتمدة على الحجم. وعلاوة على ذلك، فقد وجدنا أن الحجاج الذين عندهم عدد كبير من المعارف المتنوعة لغويا يمكن لهم أن يلعبوا دورا رئيسيا في عمليات نشر المعلومات

د تكون على درجة صغيرة من الربط على عكس شبكات الغير معتمدة على الحجم على الرغم من أنها ق -كورونا فيروس -، حيث يتم إعطاء أهمية لالدرجة والوزن. اندلاع الالتهاب الرئوي الشرق الأوسطي

يشكل تهديدا خطيرا لصحة الحجاج. لقد طبقنا نهج الشبكة نفسها لنمذجة انتشار كورونا كشبكة ذات لذي عند التحليل يمكن أن تساعد الجهات المعنية لتركيز طاقاتها نحو مجالات الاهتمام لضمان بيئة نمطين وا

صحية للحجاج.

iv

APPROVAL PAGE

The thesis of Akhlaq Ahmad has been approved by the following:

_____________________________

Mohamed Ridza Wahiddin

Supervisor

_____________________________

Mohammad Fauzan Noordin

Internal Examiner

_____________________________

Zainal Abdul Aziz

External Examiner

_____________________________

Adel Al Jumaily

External Examiner

_____________________________

Amir Akramin Bin Shafie

Chairman

v

DECLARATION

I hereby declare that this thesis is the result of my own investigations, except where

otherwise stated. I also declare that it has not been previously or concurrently submitted

as a whole for any other degrees at IIUM or other institutions.

Akhlaq Ahmad

Signature ………………………….. Date: September 26, 2017

vi

COPYRIGHT PAGE

INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA

DECLARATION OF COPYRIGHT AND AFFIRMATION OF

FAIR USE OF UNPUBLISHED RESEARCH

COMPLEX NETWORK MODELLING AND ANALYSIS OF

HAJJ CROWD AND MERS-COV OUTBREAK

I declare that the copyright holders of this thesis are jointly owned by the student

and IIUM.

Copyright © 2017 (Akhlaq Ahmad) and International Islamic University Malaysia. All rights

reserved.

No part of this unpublished research may be reproduced, stored in a retrieval system,

or transmitted, in any form or by any means, electronic, mechanical, photocopying,

recording or otherwise without prior written permission of the copyright holder

except as provided below

1. Any material contained in or derived from this unpublished research

may be used by others in their writing with due acknowledgement.

2. IIUM or its library will have the right to make and transmit copies (print

or electronic) for institutional and academic purposes.

3. The IIUM library will have the right to make, store in a retrieved system

and supply copies of this unpublished research if requested by other

universities and research libraries.

By signing this form, I acknowledged that I have read and understand the IIUM

Intellectual Property Right and Commercialization policy.

Affirmed by Akhlaq Ahmad

26th September 2017

……..…………………….. ………………………..

Signature Date

vii

DEDICATIONS

This thesis is dedicated to

My Mother

A strong and gentle soul who taught me to trust in Allah, believe in challenging work.

My Father

For earning an honest living for us and for supporting and encouraging throughout

my academic career.

viii

ACKNOWLEDGEMENTS

With a lot of love and appreciation, beyond that which I can express with words, I am

thankful to my father, my deceased mother, and to all my family members for their care

and support which keeps me going during challenging times. They have always been in

the right place at the right time with endless words of encouragement.

My special thanks to my supervisor and mentor Professor Mohamed Ridza

Wahiddin for providing me with the opportunity to work with him. His diligent

guidance over the past three years positively shaped all the aspects of my research.

Without his valuable advice, constant support and encouragement, this dissertation

would not have been in the present form. I feel very lucky to have him as my advisor. I

am thankful for his trust in me and the opportunities he gave me to grow not just as a

researcher but also as a person. Thank you, Professor Mohamed Ridza Wahiddin, thank

you for everything.

I am also very much indebted to my co-supervisor Dr Md. Abdur Rahman, for his

valuable time and feedback in this research work. His abundant enthusiasm and

optimism were a tremendous help especially during times of discouragement.

I am grateful to all my friends and colleagues for their advice and indispensable help

for the preparation of this thesis. Their guidance in technical domains made it possible

to achieve the desired results.

The work presented in this thesis was funded by King Abdul Aziz City for Science

and Technology (KACST), Kingdom of Saudi Arabia, research project through NSTIP

grant number 11-INF1683-10, 11-INF1700-10 and 13-INF2455-10.

ix

TABLE OF CONTENTS

Abstract ................................................................................................................... ii Abstract in Arabic .................................................................................................. iv

Approval Page ........................................................................................................ iv

Declaration .............................................................................................................. v Copyright Page ....................................................................................................... vi Dedications ............................................................................................................. vii Acknowledgements ................................................................................................ viii Table of Contents ................................................................................................... ix

List of Publications ................................................................................................ xiv

List of Tables .......................................................................................................... xvi List of Figures ......................................................................................................... xvii

CHAPTER ONE: INTRODUCTION .................................................................. 1 1.1 Background ................................................................................................ 1 1.2 Problem Statement ..................................................................................... 6

1.3 Research Questions .................................................................................... 8 1.4 Research Objectives ................................................................................... 9

1.4.1 Designing a Geo-Social Network ................................................... 9 1.4.2 MERS-CoV Network Modelling ................................................... 10

1.5 Research Hypothesis .................................................................................. 10

1.6 Scope of Research ...................................................................................... 11

1.7 Challenges and Proposed Solution ............................................................ 12 1.8 Scientific Contribution ............................................................................... 13 1.9 Research Process........................................................................................ 13

1.10 Data Analysis and Visualization Tool ....................................................... 16 1.11 Organization of Thesis ............................................................................... 18

CHAPTER TWO: NETWORK THEORY ......................................................... 20

2.1 Historical background ................................................................................ 20 2.2 Graph Theory ............................................................................................. 24

2.2.1 Node degree ................................................................................... 25

2.2.2 Edges .............................................................................................. 26 2.3 Complex Networks .................................................................................... 26

2.4 Types of Networks ..................................................................................... 29 2.4.1 Directed and Undirected Networks ................................................ 30

2.4.2 Weighted and unweighted Networks ............................................. 30 2.4.3 One-Mode and Two-Mode Networks ............................................ 31 2.4.4 Dynamic and static networks ......................................................... 31

2.5 Network Analysis Metrics ......................................................................... 32 2.5.1 Adjacency Matrix ........................................................................... 32

2.5.2 Network - Centrality ...................................................................... 34 2.5.3 Node Degree Centrality ................................................................. 35 2.5.4 The Betweenness Centrality .......................................................... 38 2.5.5 Closeness Centrality ....................................................................... 41 2.5.6 Eigenvector-Bonacich Power Centrality ........................................ 43

2.5.7 Centrality Comparison ................................................................... 45

x

2.5.8 Weighted Network Centrality - Node Strength ............................. 45 2.5.9 Node Degree Distribution .............................................................. 47

2.5.10 The Shortest Routes in Weighted Network ............................ 47 2.5.11 Clustering Coefficient ............................................................. 49 2.5.12 Global Clustering Coefficient ................................................. 50 2.5.13 Local Clustering Coefficient ................................................... 52 2.5.14 Network Density ..................................................................... 53

2.6 Network Models ........................................................................................ 54 2.6.1 Regular Networks .......................................................................... 55 2.6.2 ER (Erdos-Renyi) Random Network Model .................................. 56 2.6.3 WS (Watts-Strogatz) Small World Model ..................................... 59 2.6.4 Scale Free and Growing Network Models ..................................... 60

2.6.5 BA (Barabasi-Albert) Scale-Free Network Model ........................ 60 2.6.6 Weighted Scale Free Network Model (WSFN) ............................. 64

2.6.7 Fitness Based Scale Free Network Model (FBSN) ........................ 65 2.6.8 RDDWN (Reaction-Diffusion Driven Weighted Network)

Model ............................................................................................. 66 2.7 Summary .................................................................................................... 68

CHAPTER THREE: LITERATURE REVIEW ................................................. 69

3.1 Crowdsourcing ............................................................................................ 70 3.1.1 Mobile Phones as Crowdsourcing Tools ....................................... 74 3.1.2 Hajj Crowd – Related Work........................................................... 75

3.1.3 Mobile Phone Applications for Hajj-Crowd .................................. 80

3.1.4 Summary ........................................................................................ 83 3.2 Context-Awareness and Services............................................................... 84 3.3 Network Analysis ...................................................................................... 86

3.3.1 Network Modelling and Analysis .................................................. 86 3.3.2 Information Diffusion as Services ................................................. 89

3.4 Large Gatherings Health Concerns ............................................................ 94 3.4.1 Modelling Diseases Spread ............................................................ 95 3.4.2 Diseases Data Analytics ................................................................. 98

3.4.3 Diseases Network Analysis ............................................................ 100 3.5 Summary .................................................................................................... 102

CHAPTER FOUR: HAJJ – A SPATIO-TEMPORAL EVENT ....................... 103

4.1. Introduction ................................................................................................ 103 4.2. Spatio-Temporal Zones.............................................................................. 104

4.2.1 Spatial Zones .................................................................................. 104

4.2.2 Temporal Zones ............................................................................. 105 4.3. System Architecture ................................................................................... 106

4.3.1 High-Level Crowdsourcing Environment ...................................... 107 4.3.2 Crowd-Sourced Data Classification ............................................... 109

4.4 Context Modelling ..................................................................................... 111

4.4.1 Case-I: The pilgrim is outside the SZ4 ........................................... 112 4.4.2 Case-II: The pilgrim is inside the SZ4 ........................................... 114

4.5 System Implementation ............................................................................. 115 4.5.1 Front-End Application ................................................................... 115

4.5.2 Pool of Services ............................................................................. 116

xi

4.5.3 Server-Side Architecture ................................................................ 118 4.5.4 POI Collection ............................................................................... 120

4.6 System Evaluation ..................................................................................... 122 4.6.1 Overall Rating ................................................................................ 123 4.6.2 System Usability ............................................................................ 124 4.6.3 Services Details .............................................................................. 125

4.7 Summary .................................................................................................... 127

CHAPTER FIVE: MODELLING HAJJ CROWD AS HAJJ GEO-

SOCIAL NETWORK (HGSN) ............................................................................. 128 5.1 Methodology to Build HGSN .................................................................... 129

5.1.1 Adjacency Matrix ........................................................................... 131

5.1.2 HGSN Network Visualization ....................................................... 132 5.1.3 Hubs in HGSN Network ................................................................ 133

5.2 HGSN Preliminary Data-Set Analysis ....................................................... 134 5.3 HGSN Network Overview ......................................................................... 135

5.3.1 Network Density ............................................................................. 135 5.3.2 Network Centralization .................................................................. 136

5.3.3 Network Diameter .......................................................................... 136 5.3.4 Connected Components ................................................................. 137

5.4 HGSN Node Overview .............................................................................. 138 5.4.1 Node Strength ................................................................................. 138 5.4.2 Degree Centrality ........................................................................... 142

5.4.3 Betweenness Centrality .................................................................. 142

5.4.4 Closeness Centrality ....................................................................... 144 5.4.5 Eigenvector Centrality ................................................................... 144

5.5 HGSN Edge Overview............................................................................... 144

5.5.1 Edge-Betweenness .......................................................................... 145 5.5.2 Average Path Length ...................................................................... 146

5.6 HGSN Power Law Behaviour.................................................................... 147 5.7 Summary .................................................................................................... 150

CHAPTER SIX: INFORMATION DIFFUSION IN (HGSN) ........................... 151 6.1 HGSN is a Multilingual network ............................................................... 152 6.2 Visualization of Multilingual HGSN ......................................................... 153

6.3 HGSN Subnets ........................................................................................... 154

6.4 HGSN Multilingual Hubs .......................................................................... 155 6.5 Algorithm to find Multilingual Hubs ......................................................... 156 6.6 Hubs Connectivity Graph .......................................................................... 157

6.7 Network Coverage ..................................................................................... 159 6.8 Multilingual Hubs Network ....................................................................... 160

6.8.1 HGSN-MLH Network-Analyses.................................................... 161 6.8.2 Node analysis for HGSN-MLH Network ...................................... 162

6.9 Information Diffusion Process ................................................................... 164

6.10 Summary .................................................................................................... 166

CHAPTER SEVEN: MODELLING MERS-COV AS TWO-MODE

NETWORK ............................................................................................................ 167

7.1 MESR-CoV Outbreak ................................................................................ 169

xii

7.2 MESR-CoV in Saudi Arabia ..................................................................... 170 7.3 Two-Mode Networks ................................................................................. 172

7.4 Modelling MERS-CoV as a Two-Mode Network ..................................... 172 7.4.1 Hubs in MERS-CoV Network ....................................................... 173 7.4.2 MERS-CoV Network Basic Analysis ............................................ 175

7.5 Network Projection (Two-Mode to One-Mode) ........................................ 176 7.6 MERS-CoV Two-Mode to One-Mode Network Projection ...................... 179

7.7 W-Projection of two-mode MERS-CoV.................................................... 180 7.7.1 Projection by Binary Method ......................................................... 180 7.7.2 Projection by Weighted Binary Method ........................................ 182 7.7.3 Projection by Newman Binary Method ......................................... 182 7.7.4 Projection by Pardon’s Method ..................................................... 183

7.7.5 Projection by Opsahl Method ........................................................ 184 7.8 W- MERS-CoV Network - Primary Analysis and Visualization .............. 185

7.9 Summary .................................................................................................... 187

CHAPTER EIGHT: TWO-MODE MERS-COV NETWORK

PROJECTION AND ANALYSIS ........................................................................ 188

8.1 MERS-CoV Two-Mode Weighted Network ............................................. 189 8.2 L-Projection of MERS-CoV Network ....................................................... 190

8.2.1 Hubs in L-MERS-CoV Network .................................................... 192 8.2.2 L-MERS-CoV Network Metrics .................................................... 192

8.3 L-MERS-CoV Network Analysis .............................................................. 193

8.3.1 L-MERS-CoV Network Overview ................................................ 193

8.3.2 L-MERS-CoV Network-Density ................................................... 194 8.3.3 L-MERS-CoV Network-Centralization ......................................... 194 8.3.4 L-MERS-CoV Network-Diameter ................................................. 194

8.3.5 Connected Components in L-MERS-CoV ..................................... 195 8.4 L-MERS-CoV Nodal Analysis .................................................................. 195

8.4.1 L-MERS-CoV Node Strength ........................................................ 196 8.4.2 L-MERS-CoV Degree Centrality................................................... 199 8.4.3 L-MERS-CoV Betweenness Centrality ......................................... 199

8.4.4 L-MERS-CoV Closeness Centrality .............................................. 201 8.4.5 L-MERS-CoV Eigenvector Centrality ........................................... 202 8.4.6 Edge Overview ............................................................................... 202

8.4.7 L-MERS-CoV Edge-Betweenness ................................................. 203

8.4.8 L-MERS-CoV Average Path Length ............................................. 204 8.5 Five Important Locations in L-MERS-CoV Network ............................... 205 8.6 L-MERS-CoV Power Law Behaviour ....................................................... 207

8.7 Clustering In L-MERS-CoV Network ....................................................... 210 8.8 Robustness Under (Random Links Removal) in SFN ............................... 211 8.9 Summary .................................................................................................... 214

CHAPTER NINE: RESULTS AND DISCUSSION ........................................... 215

9.1 Hajj Crowd’s needs and mobilIty .............................................................. 216 9.1.1 Findings .......................................................................................... 216 9.1.2 Discussion ...................................................................................... 218

9.2 HGSN and Information Difussion ............................................................. 220

9.2.1 Findings .......................................................................................... 220

xiii

9.2.2 Discussions .................................................................................... 221 9.2.2.1 Information Diffusion Simulation........................................... 221

9.2.2.2 Edge Betweenness Effect on Diffusion .................................. 225 9.2.2.3 Scalability of HGSN ............................................................... 227

9.3 MERS-CoV Network Moel and Analsys................................................... 228 9.3.1 Findings .......................................................................................... 229 9.3.2 Discussion ...................................................................................... 232

9.4 Thesis Summary ........................................................................................ 233 9.5 Future research ........................................................................................... 234

REFERENCES ....................................................................................................... 236 Appendix A ............................................................................................................. 250

Appendix B ............................................................................................................. 252 Appendix C ............................................................................................................. 253

Appendix D ............................................................................................................. 254 Appendix E ............................................................................................................. 255 Appendix F ............................................................................................................. 256 Appendix G ............................................................................................................. 258

Appendix H ............................................................................................................. 259 Appendix I .............................................................................................................. 260

Appendix J .............................................................................................................. 262 Appendix K ............................................................................................................. 263

xiv

LIST OF PUBLICATIONS

Akhlaq Ahmad, Md. Abdur Rahman, Faizan Ur Rehman, Imad Afyouni, Bilal Sadiq,

Mohamed Ridza Wahiddin, "Towards a Mobile and Context-Aware Framework from

Crowdsourced Data", The 5th International Conference on Information and Communication

Technology for The Muslim World (ICT4M’14), Nov. 2014, Kuching Malaysia).

Akhlaq Ahmad, Md. Abdur Rahman, Bilal Sadiq, Shady Mohammed, Saleh Basalamah,

Mohamed Ridza Wahiddin, “Visualisation of a Scale-Free Network in a Smartphone-based

Multimedia Big Data Environment”, Big MM 2015, The First IEEE international Conference

on Multimedia Big Data, 20-22 April 2015, Beijing, China.

Akhlaq Ahmad, Imad Afyouni, Abdullah Murad, Md. Abdur Rahman, Faizan Ur Rehman,

Bilal Sadiq, Mohamed Ridza Wahiddin, “Quality and Context-Aware Data Collection

Architecture from Crowd-Sourced Data”, in Proceedings of the Fourth International Multi-

topic Conference (IMTIC’15), Feb 2015, Pakistan.

Akhlaq Ahmad, Md. Abdur Rahman, Faizan Ur Rehman, Ahmed Lbath, Imad Afyouni,

Abdelmajid Khelil, Syed Osama Hussain, Bilal Sadiq, Mohamed Ridza Wahiddin, “A

framework for crowd-sourced data collection and context-aware services in Hajj and Umrah,".

12th ACS/IEEE International Conference on Computer Systems and Applications AICCSA’14,

Nov. 2014, Doha Qatar.

Akhlaq Ahmad, Faizan Ur Rehman, Md. Abdur Rahman, Abdullah Murad, Bilal Sadiq,

Ahmad Qamar, Salah Basalamah, Mohamed Ridza Wahiddin, “i-Diary: A Crowdsource-based

Spatio-Temporal Multimedia Enhanced Points of Interest Authoring Tool”, Proceeding of

ACM Multimedia (demo paper), ACMMM’15 October 2015, Brisbane, Australia.

Akhlaq Ahmad, Imad Afyouni, Abdullah Murad, Md. Abdur Rahman, Faizan Ur Rehman,

Bilal Sadiq, Saleh Basalamah, Mohamed Ridza Wahiddin, “ST-Diary: A Multimedia Authoring

Environment for Crowdsourced Spatio-Temporal Events”, Proceedings of 8th ACM

SIGSPATIAL International Workshop on Location-Based Social Networks “LBSN’15”,

November 2015, Bellevue, WA, USA,

Akhlaq Ahmad, Mohamed Ridza Wahiddin, Md. Abdur Rahman, Imad Afyoni, Bilal Sadiq,

Faizan ur Rahman, Sohaib Ghani, “Scale-Free Network Analysis of a Large Crowd through

their Spatio-Temporal Activities”, 4th International Conference on Advanced Computer Science

Applications and Technologies ACSAT 15, Dec. 2015, Kuala Lumpur Malaysia)

Akhlaq Ahmad, Md. Abdur Rahman, Mohamed Ridza Wahiddin, Faizan Ur Rehman,

Abdelmajid Khelil, Ahmed Lbath, “Context-Aware Services based on Spatio-Temporal Zoning

and Crowdsourcing”, Journal of Behavior and Information Technology, Tailor Francis Series,

Indexed by Thomson Reuters, IF 1.211, (To appear)

xv

Faizan Ur Rehman, Ahmed Lbath, Bilal Sadiq, Akhlaq Ahmad, Abdullah Murad, Imad Afyouni,

Md. Abdur Rahman, Saleh Basalamah, “Constraint-Aware Optimized Path Recommender in

Crowdsourced Environment”. 12th ACS/IEEE International Conference on Computer Systems

and Applications AICCSA 2015, November 17-20, 2015, Marrakech, Morocco.

Bilal Sadiq, Faizan Ur Rehman, Akhlaq Ahmad, Abdullah Murad, Ahmad Lbath, Md. Abdur

Rahman, Sohaib Ghani, “A Spatio-temporal Multimedia Big Data Framework for a Large

Crowd”. Proceeding of IEEE International Conference on Big Data (IEEE Big Data 2015),

October 29-November 1, 2015, Santa Clara, CA, USA

xvi

LIST OF TABLES

Table 2-1 Node Centralities Comparison 45

Table 2-2 Global Clustering Measures 51

Table 2-3 Global Clustering Coefficient of a Weighted Network 52

Table 2-4 Node Degrees - Probabilities 63

Table 3-1Crowdsourcing Techniques – A Summary 74

Table 3-2 Smartphone Apps with Multiple Services Offered. A Comparison 82

Table 4-1 Spatial Zones in The City of Makkah 105

Table 4-2 Service Usage Chart During Hajj 126

Table 5-1 HGSN Network Analysis Metrics 134

Table 5-2 HGSN Node Strength 140

Table 5-3 Node Centralities of HGSN 143

Table 5-4 HGSN, No. of Nodes with Node Degree and Degree Distribution 147

Table 6-1 Nationality Degree for Few of The Nodes In HGSN 159

Table 6-2 Network Coverage by Selecting Multilingual Pilgrims 160

Table 6-3 HGSN-Multilingual Hubs Network Analysis Metrics 161

Table 6-4 HGSN-MLH Network- Node Centralities and Strength 163

Table 7-1 MERS-CoV Two-Mode Network – A Few Metrics Values 176

Table 7-2 MERS-CoV W-Projection Network Analysis Metrics 185

Table 8-1 Hubs (Locations with higher Degree) in L-MERS-CoV Network 192

Table 8-2 L-MERS-CoV Network Analysis Metrics 193

Table 8-3 L-MERS-CoV Network Node Strength 197

Table 8-4 Node Centralities Of L-MERS-CoV Network 200

Table 8-5 Hajj Related Locations In L-MERS-CoV Network 206

Table 8-6 L-MERS-CoV Network, No. Of Nodes With Degree 208

Table 9-1 Users’ Services Ratings (Five Scales) 217

Table 9-2 Number of Nodes Receiving Information at Every Step (1-10) 225

xvii

LIST OF FIGURES

Figure 1-1 Number of Pilgrims (1996 – 2014) (1427 H- 1436 H) 2

Figure 1-2 Network Between Pilgrims and Services (Hajj Geo-Social Network) 3

Figure 1-3 MERS-CoV Cases in Saudi Arabia 4

Figure 1-4 Airports in the MERS-CoV Source Region 5

Figure 1-5 Research Phases 14

Figure 1-6 Gephi (Open Source Software for Network Analysis) 17

Figure 2-1 Konigsberg Bridge Model (Hopkins and Wilson 2007) 21

Figure 2-2 Moreno's Sociogram Of A 1st-Grade Class 21

Figure 2-3 DGG Women-By-Events Matrix 22

Figure 2-4 An Undirected Network, Showing Edges Between Nodes 24

Figure 2-5 A Graph Labelled by The Degrees of Nodes 25

Figure 2-6 (a) In-Degree 3, (b) Out-Degree 2 25

Figure 2-7 (a) Social Network, (b) Ingredient Network, (c) Air Flight Network 28

Figure 2-8 Different Types of Networks. 29

Figure 2-9 An Undirected Graph and Its Adjacency Matrix 33

Figure 2-10 A Directed Graph and Its Adjacency Matrix 33

Figure 2-11 A Weighted Directed Graph and Its Adjacency Matrix 34

Figure 2-12 Red-Colored Nodes Have more Connectivity 35

Figure 2-13 (a) & (b) Networks with Nodes’ Normalised Degree Centralities 36

Figure 2-14 Freeman’s Network Centralization Measure 37

Figure 2-15 The Butterfly Network (a) Node Degrees (b) Betweenness Centrality 38

Figure 2-16 Betweenness Centrality Of The Node B is Higher 39

Figure 2-17 Networks with Closeness Centralities. Value for Node A is More 41

Figure 2-18 A Network with Node "𝑖" Surrounded by Three Neighbours 43

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Figure 2-19 Bonachich Power Centrality 44

Figure 2-20 Weighted Network. Nodes A, C and E with Equal Weighted Links 46

Figure 2-21 A Weighted Network 48

Figure 2-22 Weighted Undirected Network-Strongest Ties within The Triangle 50

Figure 2-23 Local Clustering Coefficient 53

Figure 2-24 Network Models History 54

Figure 2-25 (a) 1-Regular graph, (b) 2-Regular graph (c) 3-Regular graph 55

Figure 2-26 ER-Random Network Model with Probability Distribution 56

Figure 2-27 Poison’s Distribution Curve for ER-Networks 57

Figure 2-28 Probability Distribution - Cars Passing a Point in Any Minute 57

Figure 2-29 Three Basic Network Types in The Model of Watts and Strogatz 59

Figure 2-30 Power-Law Vs Poison’s Distribution 62

Figure 2-31 Preferential Attachment. New Node Gets Attached with Rich Node 62

Figure 2-32 Power-Law Behaviour 64

Figure 2-33 (a) Reaction-Diffusion Process (b) & (c) Resource-Driven Weight

Dynamics 67

Figure 3-1 Related Work – Structure 69

Figure 3-2 Worldwide Smartphone Penetration 80

Figure 4-1 Spatial Zones in The Holy City of Makkah 104

Figure 4-2 Temporal Zones TZ1 - TZ5 with Spatial Zones SZ1 – SZ6 105

Figure 4-3 High-Level System Architecture 107

Figure 4-4 Sensory, User-Generated, and Social Data 110

Figure 4-5 A Pilgrim (i) Outside and (ii) Inside the Spatial Zone SZ4 112

Figure 4-6 Perform Hajj and Umrah App, Out of Boundary Service 116

Figure 4-7 Hajj and Umrah App, An Overview 116

Figure 4-8 Server-Side Architecture 119

Figure 4-9 (a) User Interface to Add POI, (b) POI Metadata and Submit 121

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Figure 4-10 (a) POIs Filter (b) Visualisation for Filtered POI (Nearby Hotels) 122

Figure 4-11 POI(s) Collected from the Pilgrims’ Crowdsourced Data 122

Figure 4-12 Overall Rating 123

Figure 4-13 Users’ Experience About System Usability 125

Figure 4-14 No. of Users Seen On 21st Sep - 8th Oct, During Hajj 2014 Event 125

Figure 4-15 Users’ Satisfaction for Services Offered - Five Evaluation Measures 126

Figure 5-1 Pilgrims Presence in Different Spatial Zones, 2014 Hajj Event 128

Figure 5-2 Pilgrims Communicating Through Hajj Messenger/SMS Services 129

Figure 5-3 Pilgrims’ (a) & (b) Chat Screens, (c) Location & (d) SMS Service 130

Figure 5-4 Visualisation of HGSN Captured During Hajj 2014 Event 132

Figure 5-5 Visualisation of Single Node Within HGSN 133

Figure 5-6 Network Overview 135

Figure 5-7 Five Connected Components - 5 Distinct Colours 137

Figure 5-8 Node Overview-Metrics to Be Applied for The Nodal Analysis 138

Figure 5-9 Nodal Strength - Varying Tuning Parameter , (Opsahl Method) 141

Figure 5-10 Node Strength-A Comparison. Barrat Vs Opsahl Method 1.5 141

Figure 5-11 Edge Overview -Metrics to Be Applied for Edge Analysis 145

Figure 5-12 Edge-Betweenness Vs Edge Weight 146

Figure 5-13 HGSN, Node Degree Follows SFN Behaviour 149

Figure 6-1 HGSN- Comparison Between Pilgrims from Different Nationalities 152

Figure 6-2 HGSN, A Multilingual Network 154

Figure 6-3 Multilingual Hubs- A pilgrim with Nationality (a) Canada (b) Egypt 155

Figure 6-4: Algorithm to find Multilingual Hubs 156

Figure 6-5 Pilgrims with Nationality Degree 158

Figure 6-6 HGSN-Multilingual Hubs Network (HGSN-MLH Network) 161

Figure 6-7: Information Diffusion Process 165

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Figure 7-1 Confirmed Global MERS-CoV Cases Reported (1/2012 to 9/2016) 169

Figure 7-2 KSA Cities-Mostly Affected By MERS-CoV 170

Figure 7-3 MERS-CoV Fatality Rate in KSA Over Past Two Years 171

Figure 7-4 MERS-CoV Two-Mode Network (Between Locations and Weeks) 173

Figure 7-5 L46 is Mostly Under Attack During Different - 103 weeks 174

Figure 7-6 During Week 93, MERS-CoV Cases Were Reported At13 Locations 174

Figure 7-7 In Week 93, There Are 39 MERS-CoV Cases Reported -13 Locations 175

Figure 7-8 A Two-Mode Network (An example) 176

Figure 7-9 One-Mode P-Projection (a) Unweighted Projection, Simple Binary

Method, (b) Weighted Projection-The Binary Weighted Method 177

Figure 7-10 One-Mode S-Projection (a) Unweighted Projection, Simple Binary

Method, (b) Weighted Projection-The Binary Weighted Method 178

Figure 7-11 (a) 2-Mode Network, and Edges (b)Without Weights (c)With Weights

181

Figure 7-12 (a) 2-Mode Network, (b) W-MERS-CoV-N-Weighted Binary Mthd 182

Figure 7-13 (a) 2-Mode Network, (b) W-MERS-CoV-N-Newton Binary Method 183

Figure 7-14 (a) Two Mode Network, (b) W-MERS-CoV-N-Pardon’s method 184

Figure 7-15 (a) Two Mode Network, (b) W-MERS-CoV-N - Opsahl method 185

Figure 7-16 W-MERS-CoV Network (W-Projection) 186

Figure 8-1 MERS-CoV Two-Mode Weighted Network 189

Figure 8-2 Edges with Weights: Subnets of (a) Week W104, (b) Location L27 190

Figure 8-3 Visualisation of L-MERS-CoV Network (L-Projection) 190

Figure 8-4 Visualisation of Weighted Subnet of Location 25 191

Figure 8-5 L-Projected MERS-CoV Network Overview 193

Figure 8-6 L-MERS-CoV Network Nodal Analysis Overview 195

Figure 8-7 Nodal Strength with Variable Tuning Parameter , (Opsahl Method) 198

Figure 8-8 Node Strength, A Comparison: Barrat Vs Opsahl Method 0.5 199

Figure 8-9 L-MERS-CoV Network Edge Overview 203

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Figure 8-10 L-MERS-CoV Network - Edge-Betweenness Vs Edge Weight 203

Figure 8-11 Five Locations Comparison with Entire L-MERS-CoV Network 205

Figure 8-12 L-MERS-CoV Network Plots for Degree Distribution 209

Figure 8-13 Betweenness Centralities (L37 and L38) 212

Figure 8-14 Closeness Centralities of L37 and L38 213

Figure 8-15 Betweenness Vs Closeness Centralities of L37 and L38 213

Figure 9-1 System’s Usability - Users’ Feedback 216

Figure 9-2 HGSN Simulation Window 221

Figure 9-3 Information Diffusion in HGSN 222

Figure 9-4 Number of Nodes During Diffusion Steps 223

Figure 9-5 (a-h) Nodes During Diffusion Steps Through Some Important Nodes 224

Figure 9-6 Stepwise Comparison, Diffusion Process for Few Important Nodes 225

Figure 9-7 Diffusion Comparison Before and After removing High Betweenness

Edge for (a) Node Id 926 and Id 926 226

Figure 9-8 (c) Scalability with Probability Distribution is Scale Free 228

Figure 9-9 Number of MERS-CoV Case Reported (in Weeks) 229

Figure 9-10 Locations (a) Affected Vs Weeks (b) For Week 93 230

1

CHAPTER ONE

INTRODUCTION

1.1 BACKGROUND

Increasing travelling facilities have mobilised a significant percentage of world

population for sports events, leisure trips, education, adventure, medical treatments and

pilgrimage. Mostly in sports or religion-related activities, people count reaches millions

or even more. A few of the large gatherings1 are Kumbh Mela (2013), gathering at

shrine of Husayn Ibn Ali, (2013), funeral of C. N. Annadurai2 (1969), funeral of

Ayatollah Khomeini3 (1989), concert given by Rod Stewart (1994), Hajj4 gathering,

protest in Circus Maximus, Rome against government of Silvio Berlusconi5 (2002).

For all such large gatherings, human mobility is constrained to some spatio-

temporal activities. Monitoring human mobility is an essential part of providing the

necessary services for such activities but it comes with multiple challenges such as

disaster/crisis management and public safety (Kantarci et al. 2015), that becomes more

crucial for cultural and linguistically diverse crowds. People in the crowd, interact with

their community of interest on an ad-hoc basis and with different available services,

thereby constituting a network on interest (by considering individuals and services as

nodes and the interaction between them the edges). This network (Geo-Socio Network)

can be captured and by applying suitable network analysis metrics, a valuable

information can be generated to be used for better crowd management. In such

1 https://en.wikipedia.org/wiki/List_of_largest_peaceful_gatherings 2 Former Chief minister of Tamil Nadu, India 3 Iranian religious leader. 4 Fifth pillar of Islam, an annual Islamic pilgrimage to Mecca, and a mandatory religious duty for Muslims that

must be carried out at least once in their lifetime by all adult Muslims who are physically and financially capable

of undertaking the journey. 5 An Italian media tycoon and politician, former Prime Minister of Italy.

2

networks, highly connected people who can help in the quick diffusion of information

can be found within the entire network. They can help in spreading the information

about some serious epidemic threat and or guidelines for some emergency situations.

Moreover, if the crowd is linguistically diverse, even people with less connectivity but

knowing more than one language can play a significant role in bridging two or more

linguistically different groups in Information Diffusion process. Among few large

gatherings, Hajj is a yearly activity for a short duration of about a week, where pilgrims

from all over the world speaking multiple languages, get together to perform Hajj rituals

within certain spatio-temporal zones. This heterogeneous crowd comprises of millions

of pilgrims who perform a series of spatio-temporal obligatory rituals in a duration of

about a week. (Figure 1-1), below shows the Hajj Crowd count over the last decade

(1427 H – 1436 H, Islamic Year). The slight decrease at the end for a couple of years is

due to building the new infrastructure.

Figure 1-1 Number of Pilgrims (1996 – 2014)6 (1427 H- 1436 H)

6 Central Department of Statistics and Information, KSA

3

Facilitating Hajj crowd with necessary services is the priority for the local management.

Different government ministries are working on their portfolios for providing necessary

services to provide comfort to the pilgrims during Hajj. Pilgrims interact with provided

services such as navigation to their favourite points of interests, hospitals, restaurants,

currency exchange etc., thereby constitutes a pilgrims’ services network as shown in (

Figure 1-2). In addition, they interact with other pilgrims and family members back in

their home countries which inturn establishing a social network (pilgrim-pilgrim social

network). Both these Geosocial networks, pilgrims-pilgrims network, and pilgrims-

services network can be studied by applying suitable network analysis metrics to get an

overview of mobility patterns and thereby focusing on arrangements for crowd’s

comfort. More specifically although dissemination of important information can be

attained, it is always challenging due to the cultural and linguistic diversity of Hajj

crowd (Hameed 2010).

Figure 1-2 Network Between Pilgrims and Services (Hajj Geo-Social Network)