Social Network Mining with Nonparametric Relational Models

2010 ◽  
pp. 77-96 ◽  
Author(s):  
Zhao Xu ◽  
Volker Tresp ◽  
Achim Rettinger ◽  
Kristian Kersting
Keyword(s):  
Social Network ◽  
Relational Models ◽  
Network Mining ◽  
Social Network Mining

An integrated social network mining for product-based technology analysis of Apple

2017 ◽  
Vol 117 (10) ◽  
pp. 2417-2430 ◽  
Author(s):  
Juhwan Kim ◽  
Sunghae Jun ◽  
Dong-Sik Jang ◽  
Sangsung Park
Keyword(s):  
Social Network ◽  
Product Development ◽  
New Product ◽  
Development Planning ◽  
Patent Analysis ◽  
Content Type ◽  
Network Mining ◽  
Technology Analysis ◽  
Social Network Mining ◽  
A Company

Purpose Patent contains vast information on developed technologies because of the patent system. So, it is important to analyze patent data for understanding technologies. Most previous studies on patent analysis were focused on the technology itself. Their research results lacked the consideration of products. But the patent analysis based on products is crucial for company because a company grows by sales of competitive products. The purpose of this paper is to propose a novel methodology of patent analysis for product-based technology. This study contributes to the product development strategy of a company. Design/methodology/approach The primary goal for developing technology is to release a new product. So it is important to analyze the technology based on the product. In this study, the authors analyze Apple’s technologies based in iPod, iPhone, and iPad. In addition, the authors propose a new methodology to analyze product-based technology. The authors call this an integrated social network mining (ISNM). In the ISNM, the authors carry out a social network analysis (SNA) according to each product of Apple, and integrate all SNA results of iPod, iPhone, and iPad using the technological keywords. Findings In this case study, the authors analyze Apple’s technologies according to Apple’s innovative products, such as the iPod, iPhone, and iPad. From the ISNM results of Apple’s technology, the authors can find which technological detail is more important in overall structure of Apple’s technologies. Practical implications This study contributes to the management of technology including new product development, technological innovation, and research and development planning. To know the technological relationship between whole technologies based on products can be the source of intensification of technological competitiveness. Originality/value Most of studies on technology analysis were focused on patent technology itself. Though one of their research goals was to develop new product, they had their limits considering the products because they did not use the technology information in the technology analysis. The originality of this research is to use the product information in technology analysis using the proposed ISNM.

Mining Organizations’ Networks

2011 ◽  
pp. 205-230 ◽  
Author(s):  
James A. Danowski
Keyword(s):  
Social Network ◽  
Oil Spill ◽  
Organizational Hierarchy ◽  
Art And Design ◽  
Bp Oil Spill ◽  
Network Mining ◽  
Social Network Mining ◽  
Corporate Innovation ◽  
Semantic Associations ◽  
Over Time

This chapter presents six examples of organization-related social network mining: 1) interorganizational and sentiment networks in the Deepwater BP Oil Spill events, 2) intraorganizational interdepartmental networks in the Savannah College of Art and Design (SCAD), 3) who-to-whom email networks across the organizational hierarchy the Ford Motor Company’s automotive engineering innovation: “Sync® w/ MyFord Touch”, 4) networks of selected individuals who left that organization, 5) semantic associations across email for a corporate innovation in that organization, and 6) assessment of sentiment across its email for innovations over time. These examples are discussed in terms of motivations, methods, implications, and applications.

Social Network Mining for Predicting Users’ Credibility with Optimal Feature Selection

2021 ◽  
pp. 361-373
Author(s):  
P. Jayashree ◽  
K. Laila ◽  
K. Santhosh Kumar ◽  
A. Udayavannan
Keyword(s):  
Feature Selection ◽  
Social Network ◽  
Network Mining ◽  
Social Network Mining ◽  
Optimal Feature Selection ◽  
Optimal Feature

Social Network Mining from the Web

2011 ◽  
pp. 149-175 ◽  
Author(s):  
Yutaka Matsuo ◽  
Junichiro Mori ◽  
Mitsuru Ishizuka
Keyword(s):  
Social Network ◽  
Search Engine ◽  
Relevant Information ◽  
Web Content ◽  
Message Boards ◽  
Network Information ◽  
Network Mining ◽  
Social Network Mining ◽  
The Social ◽  
The Web

This chapter describes social network mining from the Web. Since the end of the 1990s, several attempts have been made to mine social network information from e-mail messages, message boards, Web linkage structure, and Web content. In this chapter, we specifically examine the social network extraction from the Web using a search engine. The Web is a huge source of information about relations among persons. Therefore, we can build a social network by merging the information distributed on the Web. The growth of information on the Web, in addition to the development of a search engine, opens new possibilities to process the vast amounts of relevant information and mine important structures and knowledge.

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