scholarly journals Using dual-network-analyser for communities detecting in dual networks

2021 ◽  
Vol 22 (S15) ◽  
Author(s):  
Pietro Hiram Guzzi ◽  
Giuseppe Tradigo ◽  
Pierangelo Veltri
Keyword(s):  
Biological Networks ◽  
Biological Data ◽  
Network Mining ◽  
Meaningful Information ◽  
Social Network Mining ◽  
Research Fields ◽  
Conceptual Network ◽  
Network Analyser ◽  
Connected Subgraphs ◽  
Mapping Information

Abstract Background Representations of the relationships among data using networks are widely used in several research fields such as computational biology, medical informatics and social network mining. Recently, complex networks have been introduced to better capture the insights of the modelled scenarios. Among others, dual networks (DNs) consist of mapping information as pairs of networks containing the same set of nodes but with different edges: one, called physical network, has unweighted edges, while the other, called conceptual network, has weighted edges. Results We focus on DNs and we propose a tool to find common subgraphs (aka communities) in DNs with particular properties. The tool, called Dual-Network-Analyser, is based on the identification of communities that induce optimal modular subgraphs in the conceptual network and connected subgraphs in the physical one. It includes the Louvain algorithm applied to the considered case. The Dual-Network-Analyser can be used to study DNs, to find common modular communities. We report results on using the tool to identify communities on synthetic DNs as well as real cases in social networks and biological data. Conclusion The proposed method has been tested by using synthetic and biological networks. Results demonstrate that it is well able to detect meaningful information from DNs.

scholarly journals A novel algorithm for finding top-k weighted overlapping densest connected subgraphs in dual networks

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Riccardo Dondi ◽  
Mohammad Mehdi Hosseinzadeh ◽  
Pietro H. Guzzi
Keyword(s):  
Complex Systems ◽  
Physical Network ◽  
Conceptual Network ◽  
Connected Subgraphs ◽  
Novel Algorithm

AbstractThe use of networks for modelling and analysing relations among data is currently growing. Recently, the use of a single networks for capturing all the aspects of some complex scenarios has shown some limitations. Consequently, it has been proposed to use Dual Networks (DN), a pair of related networks, to analyse complex systems. The two graphs in a DN have the same set of vertices and different edge sets. Common subgraphs among these networks may convey some insights about the modelled scenarios. For instance, the detection of the Top-k Densest Connected subgraphs, i.e. a set k subgraphs having the largest density in the conceptual network which are also connected in the physical network, may reveal set of highly related nodes. After proposing a formalisation of the approach, we propose a heuristic to find a solution, since the problem is computationally hard. A set of experiments on synthetic and real networks is also presented to support our approach.

scholarly journals BioNetLink - An Architecture for Working with Network Data

2014 ◽  
Vol 11 (2) ◽  
pp. 68-79
Author(s):  
Matthias Klapperstück ◽  
Falk Schreiber
Keyword(s):  
Experimental Data ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Large Data ◽  
Biological Data ◽  
Network Data ◽  
Data Sets ◽  
Dynamic Views ◽  
Gene Regulatory ◽  
Multiple Network

Summary The visualization of biological data gained increasing importance in the last years. There is a large number of methods and software tools available that visualize biological data including the combination of measured experimental data and biological networks. With growing size of networks their handling and exploration becomes a challenging task for the user. In addition, scientists also have an interest in not just investigating a single kind of network, but on the combination of different types of networks, such as metabolic, gene regulatory and protein interaction networks. Therefore, fast access, abstract and dynamic views, and intuitive exploratory methods should be provided to search and extract information from the networks. This paper will introduce a conceptual framework for handling and combining multiple network sources that enables abstract viewing and exploration of large data sets including additional experimental data. It will introduce a three-tier structure that links network data to multiple network views, discuss a proof of concept implementation, and shows a specific visualization method for combining metabolic and gene regulatory networks in an example.

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.

An Overview of Biological Data Mining

Biotechnology ◽  
2019 ◽  
pp. 120-139
Author(s):  
Seetharaman Balaji
Keyword(s):  
Data Mining ◽  
Information Retrieval ◽  
Biological Networks ◽  
Web Mining ◽  
Biological Data ◽  
Biological Research ◽  
Digital Repository ◽  
Domain Specific ◽  
Novice Learner ◽  
Integration Data

The largest digital repository of information, the World Wide Web keeps growing exponentially and calls for data mining services to provide tailored web experiences. This chapter discusses the overview of information retrieval, knowledge discovery and data mining. It reviews the different stages of data mining and introduces the wide spread biological databanks, their explosion, integration, data warehousing, information retrieval, text mining, text repositories for biological research publications, domain specific search engines, web mining, biological networks and visualization, ontology and systems biology. This chapter also illustrates some technical jargon with picture analogy for a novice learner to understand the concepts clearly.

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.

An Overview of Biological Data Mining

2017 ◽  
pp. 130-154
Author(s):  
Seetharaman Balaji
Keyword(s):  
Data Mining ◽  
Information Retrieval ◽  
Biological Networks ◽  
Web Mining ◽  
Biological Data ◽  
Biological Research ◽  
Digital Repository ◽  
Domain Specific ◽  
Novice Learner ◽  
Integration Data

The largest digital repository of information, the World Wide Web keeps growing exponentially and calls for data mining services to provide tailored web experiences. This chapter discusses the overview of information retrieval, knowledge discovery and data mining. It reviews the different stages of data mining and introduces the wide spread biological databanks, their explosion, integration, data warehousing, information retrieval, text mining, text repositories for biological research publications, domain specific search engines, web mining, biological networks and visualization, ontology and systems biology. This chapter also illustrates some technical jargon with picture analogy for a novice learner to understand the concepts clearly.

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