social network analysis

Purpose: In this research, we aimed to identify the pattern of cooperative relations among the Pistachio producers in Damankuh rural district in Damghan. The social capital among the producers was also evaluated. Design/methodology/approach: In this paper, the cooperative relations and the social capital among the Pistachio producers in Mehmandoost, Zarrinabad and Hoseinabad Doolab, which are located in Damankooh in Damghan, were studied by applying social network analysis method. Thus, 66 people from Mehmandoost, 70 producers from Zarrinabad, and 74 farmers from Hosseinabad Dulab were studied. The relations which were studied included cooperation in exchanging farming tools, irrigation of Pistachio orchards, marketing and pest control. For analyzing these relations, we used network- level indicators of social network analysis including density, centralization, reciprocity, transitivity and Geodesic distance. These indicators were analyzed in the UCINET software. Finding: Results showed that the network macro-level indicators including density, centralization, reciprocity, transitivity and geodesic distance in studied villages were very low. This has caused problems for producers to cooperate with each other and threatens the stability of producers’ network and indicates cooperation among pistachio producers requires tremendous investment of both time and cost. Research limitations/implications: Problems like accessing farmers, distributing questionnaires among them and the long time needed in order to interview them were among the challenges faced in this research. Practical implications¬: In order to increase cooperation among the producers and social capital in their network it is suggested that farmers be instructed and informed by holding cooperative workshops, handling their problems in irrigation and pest control, running local cooperatives for supporting the farmers in the crop prices and paying attention to their demands. Originality/value: Given the importance of the studied area in the production of Pistachio, paying attention to cooperation and social capital among producers, can be a big step in using the fertility (potentiality) of this region to develop and improve the Pistachio production.

Today, due to the large volume of data and the high speed of data production, it is practically impossible to analyze data using traditional methods. Meanwhile, data mining, as one of the most popular topics in the present century, has contributed to the advancement of science and technology in a number of areas. In the recent decade, researchers have made extensive use of data mining to analyze data. One of the most important issues for researchers in this field is to identify common mainstreams in the fields of data mining and to find active research fields in this area for future research. On the other hand, the analysis of social networks in recent years as a suitable tool to study the present and future relationships between the entities of a network structure has attracted the researcher’s scrutiny. In this paper, using the method of co-occurrence analysis of words and analysis of social networks, the scientific structure and map of data mining issues in Iran based on papers indexed during the years 1388 to 1398 in the Civilica database is drawn, and the thematic trend governing research in this area has been reviewed. The results of the analysis show that in the category of data mining, concepts such as clustering, classification, decision tree, and neural network include the largest volume of applications such as data mining in medicine, fraud detection, and customer relationship management have had the greatest use of data mining techniques.

Collaboration in writing scientific articles with the growth of academic exchanges and social interactions of researchers is increasingly expanding. Scientific collaboration gives researchers the opportunity to combine the capabilities and abilities of different scientific and research disciplines, which cannot be done individually. Co-authorship is the most formal manifestation of intellectual collaboration between authors in the production of scientific research. On the other hand, the study of the trend of scientific activities and its dynamics in any specialized field is one of the most important concerns of researchers in that field. In recent years, the use of the social network analysis approach has been proposed as a suitable solution to map the scientific structure of specialized fields and the co-authorship network of researchers. In this research, the papers published in six web research conferences have been analyzed to discover the scientific network and the co-authorship based on the social network analysis approach. The results of the analysis show that in the period, concepts such as social network analysis, Internet of Things, cloud computing, and deep learning have the largest share in articles. Also, based on the number of communities formed, the authors of the conference papers were more inclined to form small scientific groups in the form of universities or research institutes of their respective organizations.

Statistics, extraction, analysis are vital in sports science. Information technology and data science will significantly increase the quality of research and decisions of sports clubs and organizations. Currently, many coaches and sports institutions use analytics and statistics that are calculated manually. Sports science shows that winning a match depends on different factors. The purpose of the research is to improve team performance by analyzing social networks, communication networks (such as players' passes and transactions during the match), and analyzing repetitive areas. These results are done by analyzing the data collected from 4 matches of the Persepolis team, including three matches from the first half of the Iranian Premier League in 2018-1399 and a Persepolis match against Al-Sharjah. This research examines the issue from two interconnected aspects: 1- Examining the performance of players individually and as part of a social network. 2- explore the communication network between players and land areas. This analysis uses the innovative method of identifying and classifying motifs.

The rise of the Internet and the exponential increase in data have made manual data summarization and analysis a challenging task. Instagram social network is a prominent social network widely utilized in Iran for information sharing and communication across various age groups. The inherent structure of Instagram, characterized by its text-rich content and graph-like data representation, enables the utilization of text and graph processing techniques for data analysis purposes. The degree distributions of these networks exhibit scale-free characteristics, indicating non-random growth patterns. Recently, word co-occurrence has gained attention from researchers across multiple disciplines due to its simplicity and practicality. Keyword extraction is a crucial task in natural language processing. In this study, we demonstrated that high-precision extraction of keywords from Instagram posts in the Persian language can be achieved using unsupervised word co-occurrence methods without resorting to conventional techniques such as clustering or pre-trained models. After graph visualization and community detection, it was observed that the top topics covered by news agencies are represented by these graphs. This approach is generalizable to new and diverse datasets and can provide acceptable outputs for new data. To the author's knowledge, this method has not been employed in the Persian language before on Instagram social network. The new crawled data has been publicly released on GitHub for exploration by other researchers. By employing this method, it is possible to use other graph-based algorithms, such as community detections. The results help us to identify the key role of different news agencies in information diffusion among the public, identify hidden communities, and discover latent patterns among a massive amount of data.