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The purpose of this paper is to analyze the wind energy technologies using the social network analysis based on patent information. Analysis of patent documents with social network analysis is used to identify the most influential and connected technologies in the field of wind energy.

In the literature, patent data are often used to evaluate technologies. Patents related to wind energy technologies are obtained from the United States Patent and Trademark Office database and the relationships among sub-technologies based on Corporate Patent Classification (CPC) codes are analyzed in this study. The results of two-phase algorithm for mining high average-utility itemsets algorithm, which is one of the utility mining algorithm in data mining, is used to find associations among wind energy technologies for social network analysis.

The results of this study show that it is very important to focus on wind motors and technologies related to energy conversion or management systems reducing greenhouse gas emissions. The results of this study imply that Y02E, F03D and F05B CPC codes are the most influential CPC codes based on social network analysis.

Analysis of patent documents with social network analysis for technology evaluation is extremely limited in the literature. There is no research related to the analysis of patent documents with social network analysis, in particular CPC codes, for wind energy technology. This paper fills this gap in the literature. This study explores technologies related to wind energy technologies and identifies the most influential wind energy technologies in practice. This study also extracts useful information and knowledge to identify core corporate patent class (es) in the field of wind energy technology.

The purpose of this paper is to review the post‐1996 literature of information science and other disciplines for the application of social network theory and social network analysis to research that provides an understanding of information environments.

The literature review involved a content analysis of 373 articles retrieved from five electronic journal databases offering broad disciplinary coverage, and a selection of nine peer‐reviewed electronic access journals in information science. Each database was limited to academic or peer reviewed journals and searched using two query phrases: social network theory (SNT) and social network analysis (SNA).

The paper demonstrates the growth of interest by information science and other disciplines in research that applies social network theory and utilizes social network analysis, indicating what research approaches and major focus trends differentiate the disciplines.

The search phrases overlook articles using social networks as the only key phrase for indexing. However, the intention was to examine the application of a theoretical concept and specific methodology, so the terms used were appropriate for this purpose.

The paper identifies opportunities to apply social network theory and social network analysis to the study of the exchange of information resources.

The paper demonstrates that information science could advance valuable contributions to an understanding of information behavior using social network theory and social network analysis as a vehicle to connect with a significant body of existing research in other disciplines.

University libraries have traditionally been the primary caretaker of scholarly resources. However, as electronic modes of information delivery replace print materials, expectations of academic libraries have evolved rapidly. In this environment, academic libraries need to be adaptable organizations. Librarianship, though, is deeply rooted in strong values and beliefs which inherently limit receptivity to change and innovation, but these constraints are not absolute. Social network research indicates that professional advice networks play a significant role in how one thinks about and performs work and that individual perspectives are broadened when diverse input is received. Based on social network analysis methods, this study explored the relationship between individual receptivity to innovation and the composition of a person's professional advice network through a purposive sample of academic librarians in Illinois. The group completed a survey that explored two dimensions: (1) the nature of relationships within their professional advice network and (2) the individual's personal receptivity to innovation. Analysis of the nature of relationships within the professional advice networks was based on a combination of quantitative and qualitative techniques, in contrast to the analysis of the respondents’ receptivity to innovation which was based on quantitative measures. Based on the information from the 440 respondents, the results of this research indicate that there is a relationship between the size of the professional advice networks and individual's receptivity to innovation, but additional aspects of the professional advice network may play a role in an individual's overall receptivity to innovation.

To provide an interesting approach for determining interval measures, through the analytic hierarchy process, for integration with social network analysis for knowledge mapping in organizations.

In order to develop improved organizational and business processes through knowledge management, a knowledge audit should be conducted to better understand the knowledge flows in the organization. An important technique to visualize these knowledge flows is the use of a knowledge map. Social network analysis can be applied to develop this knowledge map. Interval measures should be used in the social network analysis in order to determine the strength of the connections between individuals or departments in the organization. This paper applies the analytic hierarchy process to develop these interval measures, and integrates the values within the social network analysis to produce a meaningful knowledge map.

The analytic hierarchy process, when coupled with social network analysis, can be a useful technique for developing interval measures for knowledge‐mapping purposes.

The analytic hierarchy process may become tedious and arduous for use in large social network maps. More research needs to be conducted in this area for scalability.

As social network analysis is gaining more prominence in the knowledge management community, the analytic hierarchy process may be able to provide more valuable measures to determine the strengths of relationships between actors than simply using ordinal numbers.

Coupling the analytic hierarchy process with social network analysis provides a novel approach for future knowledge‐mapping activities.

Most of us have experienced the “small world” phenomenon; you meet a stranger while traveling or waiting in a queue, for example, and begin to discuss where you are from, what type of work you do, or why you are at this certain location. Surprisingly, you and the stranger discover you both know the same person, possibly from your hometown, academic department, or children's school. You both remark “what a small world” and then go on your way. Small worlds are just one example of social networks, or how individuals know one another. Social scientists have been interested in this phenomenon since the 1930s and have developed network analysis tools to gain an understanding of how social networks are formed and evolve. These methods have improved significantly over the past 15 years and may provide an informative lens through which to investigate international entrepreneurship (IE).

Social Network Analysis has been posited as a useful technique to determine if leadership development programs are an effective intervention in developing social ties and enhancing connectivity among leaders in an organization. Evaluations can examine the extent to which the leadership development programs create and catalyze peer networks. This study used Social Network Analysis to evaluate the development of a peer leadership network and resulting relationships among leaders participating in a leadership development program. Several predictions were made about the development of participants’ task, career, and social networks, generally predicting enhanced “esprit de corps” with their peer leaders over time. Thirty top executives in local public health were selected to participate in a 12-month national leadership development training program. Peer network development was documented at three time points across the programmatic year at 6-month intervals. The results demonstrated that while leaders’ social networks increased over time, friendship networks increased more slowly than did acquaintance networks. The task-related networks involving interactions to solve problems, and career networks for seeking advice and support increased over time, with task-related and advice-related networks stabilizing by the end of the second workshop. Implications for developing peer leadership networks are discussed.

The authors would like to acknowledge the Robert Wood Johnson Foundation and the National Association for County and City Health Officials and for their support of this research.

The purpose of this paper is to apply link prediction to community mining and to clarify the role of link prediction in improving the performance of social network analysis.

In this study, the 2009 version of Enron e-mail data set provided by Carnegie Mellon University was selected as the research object first, and bibliometric analysis method and citation analysis method were adopted to compare the differences between various studies. Second, based on the impact of various interpersonal relationships, the link model was adopted to analyze the relationship among people. Finally, the factorization of the matrix was further adopted to obtain the characteristics of the research object, so as to predict the unknown relationship.

The experimental results show that the prediction results obtained by considering multiple relationships are more accurate than those obtained by considering only one relationship.

Due to the limited number of objects in the data set, the link prediction method has not been tested on the large-scale data set, and the validity and correctness of the method need to be further verified with larger data. In addition, the research on algorithm complexity and algorithm optimization, including the storage of sparse matrix, also need to be further studied. At the same time, in the case of extremely sparse data, the accuracy of the link prediction method will decline a lot, and further research and discussion should be carried out on the sparse data.

The focus of this research is on link prediction in social network analysis. The traditional prediction model is based on a certain relationship between the objects to predict and analyze, but in real life, the relationship between people is diverse, and different relationships are interactive. Therefore, in this study, the graph model is used to express different kinds of relations, and the influence between different kinds of relations is considered in the actual prediction process. Finally, experiments on real data sets prove the effectiveness and accuracy of this method. In addition, link prediction, as an important part of social network analysis, is also of great significance for other applications of social network analysis. This study attempts to prove that link prediction is helpful to the improvement of performance analysis of social network by applying link prediction to community mining.

This study adopts a variety of methods, such as link prediction, data mining, literature analysis and citation analysis. The research direction is relatively new, and the experimental results obtained have a certain degree of credibility, which is of certain reference value for the following related research.

The paper aims to studiy social recruiting for finding suitable candidates on social networks. The main goal is to develop a methodological approach that would enable preselection of candidates using social network analysis. The research focus is on the automated collection of data using the web scraping method. Based on the information collected from the users' profiles, three clusters of skills and interests are created: technical, empirical and education-based. The identified clusters enable the recruiter to effectively search for suitable candidates.

This paper proposes a new methodological approach for the preselection of candidates based on social network analysis (SNA). The defined methodological approach includes the following phases: Social network selection according to the defined preselection goals; Automatic data collection from the selected social network using the web scraping method; Filtering, processing and statistical analysis of data. Data analysis to identify relevant information for the preselection of candidates using attributes clustering and SNA. Preselection of candidates is based on the information obtained.

It is possible to contribute to candidate preselection in the recruiting process by identifying key categories of skills and interests of candidates. Using a defined methodological approach allows recruiters to identify candidates who possess the skills and interests defined by the search. A defined method automates the verification of the existence, or absence, of a particular category of skills or interests on the profiles of the potential candidates. The primary intention is reflected in the screening and filtering of the skills and interests of potential candidates, which contributes to a more effective preselection process.

A small sample of the participants is present in the preliminary evaluation. A manual revision of the collected skills and interests is conducted. The recruiters should have basic knowledge of the SNA methodology in order to understand its application in the described method. The reliability of the collected data is assessed, because users provide data themselves when filling out their social network profiles.

The presented method could be applied on different social networks, such as GitHub or AngelList for clustering profile skills. For a different social network, only the web scraping instructions would change. This method is composed of mutually independent steps. This means that each step can be implemented differently, without changing the whole process. The results of a pilot project evaluation indicate that the HR experts are interested in the proposed method and that they would be willing to include it in their practice.

The social implication should be the determination of relevant skills and interests during the preselection phase of candidates in the process of social recruitment.

In contrast to previous studies that were discussed in the paper, this paper defines a method for automatic data collection using the web scraper tool. The described method allows the collection of more data in a shorter period. Additionally, it reduces the cost of creating an initial data set by removing the cost of hiring interviewers, questioners and people who collect data from social networks. A completely automated process of data collection from a particular social network stands out from this model from currently available solutions. Considering the method of data collection implemented in this paper, the proposed method provides opportunities to extend the scope of collected data to implicit data, which is not possible using the tools presented in other papers.

This paper aims to systematically find the main research differences and similarities between social media and social networks in marketing research using the bibliometric perspective and provides suggestions for firms to improve their marketing strategies effectively.

The methods of co-word analysis and network analysis have been used to analyze the two research fields of social media and social networks. Specifically, this study selects 2,424 articles from 27 marketing academic journals present in the database Web of Science, ranging from January 1, 1996 to August 8, 2020.

The results show that social networks and social media are both research hotspots within the discipline of marketing research. The different intimacy nodes of social networks are more complex than social media. Additionally, the research scope of social networks is broader than social media in marketing research as shown by the keyword co-occurrence analysis. The overlap between social media and social networks in marketing research is reflected in the strong focus on their mixed mutual effects.

This paper explores the differences and similarities between social networks and social media in marketing research from the bibliometric perspective and provides a developing trend of their research hotspots in social media and social networks marketing research by keyword co-occurrence analysis and cluster analysis. Additionally, this paper provides some suggestions for firms looking to improve the efficiency of their marketing strategies from social and economic perspectives.

Is social network analysis just measures and methods with no theory? We attempt to clarify some confusions, address some previous critiques and controversies surrounding the issues of structure, human agency, endogeneity, tie content, network change, and context, and add a few critiques of our own. We use these issues as an opportunity to discuss the fundamental characteristics of network theory and to provide our thoughts on opportunities for future research in social network analysis.