Data Mining Techniques for Social Network Analysis

2019 ◽  
pp. 25-40
Author(s):  
Vijayaganth V.
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Network Data ◽  
Massive Datasets ◽  
Data Mining Techniques ◽  
Useful Knowledge ◽  
Social Network Data ◽  
Wide Range

Social networks have increased momentously in the last decade. Individuals are depending on interpersonal organizations for data, news, and the assessment of different clients on various topics. These issues often make social network data very complex to analyze manually, resulting in the persistent use of computational means for analyzing them. Data mining gives a variety of systems for identifying helpful learning from huge datasets and a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules. This chapter discusses different data mining techniques used in mining social networks.

Network Basics: Points, Lines, and Positions

2020 ◽  
pp. 15-33
Author(s):  
Ryan Light ◽  
James Moody
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Ethical Issues ◽  
Building Blocks ◽  
Levels Of Analysis ◽  
Network Data ◽  
Social Network Data ◽  
Basic Concepts ◽  
Statistical Approaches

This chapter presents an introduction to the basic concepts central to social network analysis. Written for those with little experience in the approach, the chapter aims to provide the necessary tools to dig deeper into exploring social networks via the subsequent chapters in this volume. It begins by introducing the building blocks of networks—nodes and edges—and their characteristics. Next, it outlines several of the major dimensions of network analysis, including the implications of boundary specification and levels of analysis. It also briefly introduces statistical approaches to networks and network data collection. The chapter concludes with a discussion of ethical issues that arise when collecting and analyzing social network data.

A Conceptual Framework for Social Network Data Security

2013 ◽  
pp. 58-86 ◽  
Author(s):  
Sanur Sharma ◽  
Vishal Bhatnagar
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Conceptual Framework ◽  
Social Networking Sites ◽  
Community Analysis ◽  
Network Data ◽  
Data Mining Techniques ◽  
Social Network Data ◽  
The Social ◽  
Using Data

In recent times, there has been a tremendous increase in the number of social networking sites and their users. With the amount of information posted on the public forums, it becomes essential for the service providers to maintain the privacy of an individual. Anonymization as a technique to secure social network data has gained popularity, but there are challenges in implementing it effectively. In this chapter, the authors have presented a conceptual framework to secure the social network data effectively by using data mining techniques to perform in-depth social network analysis before carrying out the actual anonymization process. The authors’ framework in the first step defines the role of community analysis in social network and its various features and temporal metrics. In the next step, the authors propose the application of those data mining techniques that can deal with the dynamic nature of social network and discover important attributes of the social network. Finally, the authors map their security requirements and their findings of the network properties which provide an appropriate base for selection and application of the anonymization technique to protect privacy of social network data.

Mining of Leaders in Mobile Telecom Social Networks

2016 ◽  
pp. 68-77
Author(s):  
Mantian (Mandy) Hu
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Information Diffusion ◽  
Network Data ◽  
Massive Datasets ◽  
Social Network Data ◽  
The Social ◽  
Marketing Efficiency ◽  
Mobile Telecom

In the age of Big Data, the social network data collected by telecom operators are growing exponentially. How to exploit these data and mine value from them is an important issue. In this article, an accurate marketing strategy based on social network is proposed. The strategy intends to help telecom operators to improve their marketing efficiency. This method is based on mutual peers' influence in social network, by identifying the influential users (leaders). These users can promote the information diffusion prominently. A precise marketing is realized by taking advantage of the user's influence. Data were collected from China Mobile and analyzed. For the massive datasets, the Apache Spark was chosen for its good scalability, effectiveness and efficiency. The result shows a great increase of the telecom traffic, compared with the result without leader identification.

scholarly journals Privacy-Preserving Assessment of Social Network Data Trustworthiness

2014 ◽  
Vol 23 (02) ◽  
pp. 1441004 ◽  
Author(s):  
Chenyun Dai ◽  
Fang-Yu Rao ◽  
Traian Marius Truta ◽  
Elisa Bertino
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Network Data ◽  
Challenging Problem ◽  
Privacy Concerns ◽  
Useful Knowledge ◽  
Social Network Data ◽  
Series Of Experiments ◽  
Data Trustworthiness

Extracting useful knowledge from social network datasets is a challenging problem. While large online social networks such as Facebook and LinkedIn are well known and gather millions of users, small social networks are today becoming increasingly common. Many corporations already use existing social networks to connect to their customers. Seeing the increasing usage of small social networks, such companies will likely start to create in-house online social networks where they will own the data shared by customers. The trustworthiness of these online social networks is essentially important for decision making of those companies. In this paper, our goal is to assess the trustworthiness of local social network data by referencing external social networks. To add to the difficulty of this problem, privacy concerns that exist for many social network datasets have restricted the ability to analyze these networks and consequently to maximize the knowledge that can be extracted from them. This paper addresses this issue by introducing the problem of data trustworthiness in social networks when repositories of anonymized social networks exist that can be used to assess such trustworthiness. Three trust score computation models (absolute, relative, and weighted) that can be instantiated for specific anonymization models are defined and algorithms to calculate these trust scores are developed. Using both real and synthetic social networks, the usefulness of the trust score computation is validated through a series of experiments.

scholarly journals A Survey of Data Mining Techniques for Social Media Analysis

2014 ◽  
Vol 2014 ◽  
Author(s):  
Mariam Adedoyin-Olowe ◽  
Mohamed Medhat Gaber ◽  
Frederic Stahl
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Data Analysis ◽  
Social Network Sites ◽  
Statistical Modelling ◽  
Data Interpretation ◽  
Media Analysis ◽  
Data Mining Techniques ◽  
Useful Knowledge ◽  
Wide Range

Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors. Comment: 25 pages, 9 figures

Social Network Analysis in Marketing

E-Marketing ◽  
2012 ◽  
pp. 185-197
Author(s):  
Przemyslaw Kazienko ◽  
Piotr Doskocz ◽  
Tomasz Kajdanowicz
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Relationships ◽  
Network Data ◽  
Social Network Data ◽  
The Social ◽  
Targeted Marketing ◽  
Potential Customers ◽  
Demographic Features

The chapter describes a method how to perform a classification task without any demographic features and based only on the social network data. The concept of such collective classification facilitates to identify potential customers by means of services used or products purchased by the current customers, i.e. classes they belong to as well as using social relationships between the known and potential customers. As a result, a personalized offer can be prepared for the new clients. This innovative marketing method can boost targeted marketing campaigns.

Social Network Analysis in Marketing

2011 ◽  
pp. 271-283
Author(s):  
Przemyslaw Kazienko ◽  
Piotr Doskocz ◽  
Tomasz Kajdanowicz
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Relationships ◽  
Network Data ◽  
Social Network Data ◽  
The Social ◽  
Targeted Marketing ◽  
Potential Customers ◽  
Demographic Features

The chapter describes a method how to perform a classification task without any demographic features and based only on the social network data. The concept of such collective classification facilitates to identify potential customers by means of services used or products purchased by the current customers, i.e. classes they belong to as well as using social relationships between the known and potential customers. As a result, a personalized offer can be prepared for the new clients. This innovative marketing method can boost targeted marketing campaigns.

Detecting Pharmaceutical Spam in Microblog Messages

2011 ◽  
pp. 101-115
Author(s):  
Kathy J. Liszka ◽  
Chien-Chung Chan ◽  
Chandra Shekar
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Cell Phones ◽  
Pharmaceutical Products ◽  
Spam Detection ◽  
Data Mining Techniques ◽  
Flow Through ◽  
Special Category

Microblogs are one of a growing group of social network tools. Twitter is, at present, one of the most popular forums for microblogging in online social networks, and the fastest growing. Fifty million messages flow through servers, computers, and cell phones on a wide variety of topics exchanged daily. With this considerable volume, Twitter is a natural and obvious target for spreading spam via the messages, called tweets. The challenge is how to determine if a tweet is a spam or not, and more specifically a special category advertising pharmaceutical products. The authors look at the essential characteristics of spam tweets and what makes microblogging spam unique from email or other types of spam. They review methods and tools currently available to identify general spam tweets. Finally, this work introduces a new methodology of applying text mining and data mining techniques to generate classifiers that can be used for pharmaceutical spam detection in the context of microblogging.

Emergent Data Mining Tools for Social Network Analysis

2015 ◽  
pp. 1539-1556
Author(s):  
Dhiraj Murthy ◽  
Alexander Gross ◽  
Alex Takata
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Online Social Networks ◽  
Data Extraction ◽  
Future Directions ◽  
Effective Development ◽  
Mining Tools

This chapter identifies a number of the most common data mining toolkits and evaluates their utility in the extraction of data from heterogeneous online social networks. It introduces not only the complexities of scraping data from the diverse forms of data manifested in these sources, but also critically evaluates currently available tools. This analysis is followed by a presentation and discussion on the development of a hybrid system, which builds upon the work of the open-source Web-Harvest framework, for the collection of information from online social networks. This tool, VoyeurServer, attempts to address the weaknesses of tools identified in earlier sections, as well as prototype the implementation of key functionalities thought to be missing from commonly available data extraction toolkits. The authors conclude the chapter with a case study and subsequent evaluation of the VoyeurServer system itself. This evaluation presents future directions, remaining challenges, and additional extensions thought to be important to the effective development of data mining tools for the study of online social networks.

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