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SOCIAL NETWORK ANALYSIS
As social media dominates our lives in increasing intensity, the need for developers to understand the theory and applications is ongoing as well. This book serves that purpose.
Social network analysis is the solicitation of network science on social networks, and social occurrences are denoted and premeditated by data on coinciding pairs as the entities of opinion.
The book features:
* Social network analysis from a computational perspective using python to show the significance of fundamental facets of network theory and the various metrics used to measure the social network.
* An understanding of network analysis and motivations to model phenomena as networks.
* Real-world networks established with human-related data frequently display social properties, i.e., patterns in the graph from which human behavioral patterns can be analyzed and extracted.
* Exemplifies information cascades that spread through an underlying social network to achieve widespread adoption.
* Network analysis that offers an appreciation method to health systems and services to illustrate, diagnose, and analyze networks in health systems.
* The social web has developed a significant social and interactive data source that pays exceptional attention to social science and humanities research.
* The benefits of artificial intelligence enable social media platforms to meet an increasing number of users and yield the biggest marketplace, thus helping social networking analysis distribute better customer understanding and aiding marketers to target the right customers.
Audience
The book will interest computer scientists, AI researchers, IT and software engineers, mathematicians.
List of contents
Preface xi
1 Overview of Social Network Analysis and Different Graph File Formats 1
Abhishek B. and Sumit Hirve
1.1 Introduction--Social Network Analysis 2
1.2 Important Tools for the Collection and Analysis of Online Network Data 3
1.3 More on the Python Libraries and Associated Packages 9
1.4 Execution of SNA in Terms of Real-Time Application: Implementation in Python 13
1.5 Clarity Toward the Indices Employed in the Social Network Analysis 14
1.5.1 Centrality 14
1.5.2 Transitivity and Reciprocity 15
1.5.3 Balance and Status 15
1.6 Conclusion 15
References 15
2 Introduction To Python for Social Network Analysis 19
Agathiya Raja, Gavaskar Kanagaraj and Mohammad Gouse Galety
2.1 Introduction 20
2.2 SNA and Graph Representation 21
2.2.1 The Common Representation of Graphs 21
2.2.2 Important Terms to Remember in Graph Representation 23
2.3 Tools To Analyze Network 24
2.3.1 MS Excel 24
2.3.2 Ucinet 26
2.4 Importance of Analysis 26
2.5 Scope of Python in SNA 26
2.5.1 Comparison of Python With Traditional Tools 27
2.6 Installation 27
2.6.1 Good Practices 28
2.7 Use Case 29
2.7.1 Facebook Case Study 30
2.8 Real-Time Product From SNA 32
2.8.1 Nevaal Maps 33
References 34
3 Handling Real-World Network Data Sets 37
Arman Abouali Galehdari, Behnaz Moradi and Mohammad Gouse Galety
3.1 Introduction 37
3.2 Aspects of the Network 38
3.3 Graph 41
3.3.1 Node, Edges, and Neighbors 41
3.3.2 Small-World Phenomenon 42
3.4 Scale-Free Network 43
3.5 Network Data Sets 46
3.6 Conclusion 49
References 49
4 Cascading Behavior in Networks 51
Vasanthakumar G. U.
4.1 Introduction 51
4.1.1 Types of Data Generated in OSNs 52
4.1.2 Unstructured Data 52
4.1.3 Tools for Structuring the Data 53
4.2 User Behavior 53
4.2.1 Profiling 54
4.2.2 Pattern of User Behavior 54
4.2.3 Geo-Tagging 55
4.3 Cascaded Behavior 56
4.3.1 Cross Network Behavior 56
4.3.2 Pattern Analysis 58
4.3.3 Models for Cascading Pattern 59
References 60
5 Social Network Structure and Data Analysis in Healthcare 63
Sailee Bhambere
5.1 Introduction 64
5.2 Prognostic Analytics--Healthcare 64
5.3 Role of Social Media for Healthcare Applications 65
5.4 Social Media in Advanced Healthcare Support 67
5.5 Social Media Analytics 67
5.5.1 Phases Involved in Social Media Analytics 68
5.5.2 Metrics of Social Media Analytics 69
5.5.3 Evolution of NIHR 70
5.6 Conventional Strategies in Data Mining Techniques 71
5.6.1 Graph Theoretic 72
5.6.2 Opinion Evaluation in Social Network 74
5.6.3 Sentimental Analysis 75
5.7 Research Gaps in the Current Scenario 75
5.8 Conclusion and Challenges 77
References 78
6 Pragmatic Analysis of Social Web Components on Semantic Web Mining 83
Sasmita Pani, Bibhuprasad Sahu, Jibitesh Mishra, Sachi Nandan Mohanty and Amrutanshu Panigrahi
6.1 Introduction 84
6.2 Background 87
6.2.1 Web 87
6.2.2 Agriculture Information Systems 88
6.2.3 Ontology in Web or Mobile Web 90
6.3 Proposed Model 90
6.3.1 Developing Domain Ontology 91
6.3.2 Building the Agriculture Ontology with OW
About the author
Mohammad Gouse Galety, PhD, is an assistant professor in the Information Technology Department, Catholic University in Erbil, Erbil, Iraq.
Chiai Al-Atroshi is a lecturer in the Educational Counseling and Psychology Department, University of Duhok, Duhok, Iraq.
Bunil Kumar Balabantaray, PhD, is an assistant professor in the Department of Computer Science and Engineering, National Institute of Technology Meghalaya, India.
Sachi Nandan Mohanty, PhD, is an associate professor in the Department of Computer Science & Engineering at Vardhaman College of Engineering (Autonomous), Hyderabad, India.
Summary
SOCIAL NETWORK ANALYSIS
As social media dominates our lives in increasing intensity, the need for developers to understand the theory and applications is ongoing as well. This book serves that purpose.
Social network analysis is the solicitation of network science on social networks, and social occurrences are denoted and premeditated by data on coinciding pairs as the entities of opinion.
The book features:
* Social network analysis from a computational perspective using python to show the significance of fundamental facets of network theory and the various metrics used to measure the social network.
* An understanding of network analysis and motivations to model phenomena as networks.
* Real-world networks established with human-related data frequently display social properties, i.e., patterns in the graph from which human behavioral patterns can be analyzed and extracted.
* Exemplifies information cascades that spread through an underlying social network to achieve widespread adoption.
* Network analysis that offers an appreciation method to health systems and services to illustrate, diagnose, and analyze networks in health systems.
* The social web has developed a significant social and interactive data source that pays exceptional attention to social science and humanities research.
* The benefits of artificial intelligence enable social media platforms to meet an increasing number of users and yield the biggest marketplace, thus helping social networking analysis distribute better customer understanding and aiding marketers to target the right customers.
Audience
The book will interest computer scientists, AI researchers, IT and software engineers, mathematicians.
