New Frontiers of Multi-Network Mining

2021 ◽  
Author(s):  
Boxin Du ◽  
Si Zhang ◽  
Yuchen Yan ◽  
Hanghang Tong
Keyword(s):  
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.

scholarly journals Fine-Grained Pests Recognition Based on Truncated Probability Fusion Network via Internet of Things in Forestry and Agricultural Scenes

Algorithms ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 290
Author(s):  
Kai Ma ◽  
Ming-Jun Nie ◽  
Sen Lin ◽  
Jianlei Kong ◽  
Cheng-Cai Yang ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Insect Pests ◽  
Accurate Identification ◽  
Technical Application ◽  
Network Mining ◽  
Fine Grained ◽  
Feature Expression ◽  
The Internet Of Things ◽  
Different Levels ◽  
Better Than

Accurate identification of insect pests is the key to improve crop yield and ensure quality and safety. However, under the influence of environmental conditions, the same kind of pests show obvious differences in intraclass representation, while the different kinds of pests show slight similarities. The traditional methods have been difficult to deal with fine-grained identification of pests, and their practical deployment is low. In order to solve this problem, this paper uses a variety of equipment terminals in the agricultural Internet of Things to obtain a large number of pest images and proposes a fine-grained identification model of pests based on probability fusion network FPNT. This model designs a fine-grained feature extractor based on an optimized CSPNet backbone network, mining different levels of local feature expression that can distinguish subtle differences. After the integration of the NetVLAD aggregation layer, the gated probability fusion layer gives full play to the advantages of information complementarity and confidence coupling of multi-model fusion. The comparison test shows that the PFNT model has an average recognition accuracy of 93.18% for all kinds of pests, and its performance is better than other deep-learning methods, with the average processing time drop to 61 ms, which can meet the needs of fine-grained image recognition of pests in the Internet of Things in agricultural and forestry practice, and provide technical application reference for intelligent early warning and prevention of pests.

Network mining: Applications to business data

2012 ◽  
Vol 16 (3) ◽  
pp. 473-490 ◽  
Author(s):  
Fatih Cavdur ◽  
Soundar Kumara
Keyword(s):  
Network Mining ◽  
Business Data

scholarly journals KnowEnG: a knowledge engine for genomics

2015 ◽  
Vol 22 (6) ◽  
pp. 1115-1119 ◽  
Author(s):  
Saurabh Sinha ◽  
Jun Song ◽  
Richard Weinshilboum ◽  
Victor Jongeneel ◽  
Jiawei Han
Keyword(s):  
Big Data ◽  
National Institutes Of Health ◽  
Knowledge Network ◽  
Data Sets ◽  
Network Mining ◽  
Construction Of Knowledge ◽  
Community Data ◽  
Science Framework ◽  
The University ◽  
Big Data Computing

Abstract We describe here the vision, motivations, and research plans of the National Institutes of Health Center for Excellence in Big Data Computing at the University of Illinois, Urbana-Champaign. The Center is organized around the construction of “Knowledge Engine for Genomics” (KnowEnG), an E-science framework for genomics where biomedical scientists will have access to powerful methods of data mining, network mining, and machine learning to extract knowledge out of genomics data. The scientist will come to KnowEnG with their own data sets in the form of spreadsheets and ask KnowEnG to analyze those data sets in the light of a massive knowledge base of community data sets called the “Knowledge Network” that will be at the heart of the system. The Center is undertaking discovery projects aimed at testing the utility of KnowEnG for transforming big data to knowledge. These projects span a broad range of biological enquiry, from pharmacogenomics (in collaboration with Mayo Clinic) to transcriptomics of human behavior.

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.

Dynamics and Evolutional Patterns of Social Networks

2011 ◽  
pp. 156-170
Author(s):  
Yingzi Jin ◽  
Yutaka Matsuo
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Real World ◽  
Dynamic Network ◽  
Network Evolution ◽  
Belief Formation ◽  
Network Mining ◽  
Longitudinal Network Analysis ◽  
Relational Network

Previous chapters focused on the models of static networks, which consider a relational network at a given point in time. However, real-world social networks are dynamic in nature; for example, friends of friends become friends. Social network research has, in recent years, paid increasing attention to dynamic and longitudinal network analysis in order to understand network evolution, belief formation, friendship formation, and so on. This chapter focuses mainly on the dynamics and evolutional patterns of social networks. The chapter introduces real-world applications and reviews major theories and models of dynamic network mining.

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