WMEgo: Willingness Maximization for Ego Network Data Extraction in Online Social Networks

2020 ◽  
Author(s):  
Bay-Yuan Hsu ◽  
Chih-Ya Shen ◽  
Ming-Yi Chang
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Data Extraction ◽  
Network Data

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.

Exploring the Properties of Online Social Network Data and Their Implications for Consumer Social Data Analytics

2014 ◽  
pp. 210-230
Author(s):  
Yamen Koubaa
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Consumer Behavior ◽  
Online Social Networks ◽  
Data Analytics ◽  
Online Social Network ◽  
Network Data ◽  
Social Data ◽  
Social Network Data ◽  
Consumer Data

The prediction of consumer behavior is largely based on the analysis of consumer data using statistics as a tool for prediction. Thanks to online social networks, large quantities of heterogeneous consumer data are now available at competitive costs. Though they have much in common with conventional data, online social network datasets display several different properties. The exploration of these unique properties is indispensable to insuring the accuracy of predictions and data analytics. This chapter presents online social data, discusses seven properties of online social network data, suggests some analysis tools, and draws implications regarding the use of social data analytics.

Emergent Data Mining Tools for Social Network Analysis

2013 ◽  
pp. 40-57
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.

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.

Important relationships in a multilevel world: The role of network structure in explaining closeness of relationships and access to resources in later life

2021 ◽  
pp. 026540752110093
Author(s):  
Mahin Raissi ◽  
Robert Ackland
Keyword(s):  
Social Networks ◽  
Statistical Model ◽  
Network Structure ◽  
Online Social Networks ◽  
Later Life ◽  
Personal Networks ◽  
Network Data ◽  
Personal Network ◽  
Access To Resources

We examine why some relationships are more important than others, using a multilevel statistical model and data on personal networks of Australians 50 years and older, collected via a purpose-built Facebook application. While the network data were collected automatically, participants in our study provided data on the importance of their relationships, measured by perceived closeness and access to resources. We find that the information on how network members are connected with each other (network structure) provides powerful insights into what makes a relationship important. When importance is measured by closeness of relationship, important alters are kin, and are alters who are highly connected with others or act as bridges between different groups. When importance is measured by access to resources, important alters are those who act as bridges and are in more densely-knit networks. We discuss our findings in a broader context of research into important relationships in later life, and collecting personal network data via online social networks.

Analysis of User Interactions in Online Social Networks

2011 ◽  
Author(s):  
Seokchan Yun ◽  
Heungseok Do ◽  
Jinuk Jung ◽  
Song Mina ◽  
Namgoong Hyun ◽  
...  
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
User Interactions
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