Introduction: Brucellosis, as the most important and most common disease between humans and animals (Zoonosis), in the field of emergence and recurrence of infectious diseases, has many damages and dangers. The control and eradication of the Brucellosis has always been considered by experts and articles and books have been published in the form of scientific collaborations. The study was to investigate the status of the "Co-authorship Social NETWORK CENTRALITY" in Brucellosis. Methods: This research is applied research that was performed using scientometric methods with an analytical approach. All Brucellosis publications indexed on the WOSCC from 1901 to 2020 cover this article's statistical population. MeSH was used to identify keywords were used to analyze the data. Excel 2016, UCINET 6. 528. 0. 0 and Netdraw software were used to analyze the data. Results: The publication trend of Bursellose territory was upward and the highest in 2019 with 398 articles. The "Journal of Clinical Microbiology" and "Blasco, Jose-Maria" were the most influential journals and authors Impact Factor and h-index. "FERREIRA, F, " "LETESSON, JJ" is in the first rank of the Degree of CENTRALITY and betweenness CENTRALITY of the Brucellosi, respectively. Five authors had the same CENTRALITY of closeness and the same chance in all citations. Conclusion: Scientific collaboration is a complex phenomenon that improves the sharing of capabilities and new scientific knowledge. Examining coauthored social NETWORKs' characteristics could provide valuable information about the essential and influential author in the brucellosis NETWORK. Study of Social NETWORKing indicators coauthors could provide valuable information about important and influential people in the Brucellosis NETWORK. The results of the above analysis are a guide for experts and scientific centers in decision-making and policy-making of scientific collaborations and budget allocation.

This study aims to optimize the portfolio using the genetic operator and NETWORK centralization. The statistical population of the study is the top 50 companies of Tehran Stock Exchange, in the first quarter of 2021, and to calculate the size of CENTRALITY, we used the difference in the overall performance of each company compared to all the top companies, based on a standard hybridization indicator. Then based on the companies’ performance in the capital market, the geometric mean of risk and return of efficient companies are determined, and given the real limitations of the budget, the requirements and expectations of the investors compared to the market’ s performance and the risk-free investment, the problem of decision-making for the composition of the investment in the form of a multi-purpose paradigm is formulated. By using the modified optimization algorithm and the genetic algorithm with dual operators, we optimized the investment’ s composition. Finally, we use the compound linear regression with data analysis approach to evaluate the effect of individual and systemic operators on determining the investment strategy, and the results represented the positive effect of these two operators.

Studying the urban street NETWORK, as one of the constituent elements of urban form and the commonality of the two systems of movement and activity, plays a significant role in understanding the dynamic events in the city and solving the problems resulted from the conflicting function of the two abovementioned systems. One of the most effective structural characteristics of the street NETWORK is street NETWORK CENTRALITY which has a substantial effect on the distribution of activities and accordingly on the formation of motorized and pedestrian traffic flow throughout the city. On the other hand, one of the most important elements influencing on the NETWORK CENTRALITY is street layout. The current article aims at explaining the relationship between street layout pattern and CENTRALITY at the local scale or the microstructure of the street NETWORK. The city of Qom is an example of an old city in Iran that has an ancient urban fabric in the central core of the city and a diverse range of street layout in the middle and peripheral parts-with distinct structural features. Thus, this city is an appropriate context as the study area to explore the microstructure of the urban street NETWORK. The research process is as follows; After identifying the relatively homogeneous central zones in terms of morphology in the study area by modelling street NETWORK CENTRALITY using Multiple CENTRALITY Assessment (MCA) method in terms of CENTRALITY index of local closeness, and applying some considered criteria, the street layout pattern of the selected zones is analyzed using several indicators of street centerline as well as blocks. Finally, the relationship between indicators of street layout pattern and the average local closeness NETWORK CENTRALITY index is explained by building a correlation matrix using Pearson’ s correlation coefficient. Findings show that just 3 out of 10 selected indicators of the street layout pattern-all of which are indicators of the NETWORK centerline-have a significant correlation with average local closeness CENTRALITY index. Therefore, average local closeness CENTRALITY index has no significant correlation with block indicators. The correlation matrix shows that the higher the NETWORK lenghth as well as the proportion of three-way intersections throughout the local fabric area, the higher the average local closeness CENTRALITY index of the street NETWORK; consequently, the more centralized fabric will be at the scale of pedestrian accessibility.

Equitable distribution of services at the regional scale and achieving a balanced spatial structure in the region are among the most important goals of sustainable regional planning, especially in developing countries such as Iran. In this regard, one of the most important strategies and policies in regional spatial planning is the decentralization of development. The purpose of this study is to identify the most important urban areas of Fars province in order to determine the priorities of development and decentralization of the unipolar development of the province. To this end, the regional NETWORK modelling based on graph theory and the concepts of social NETWORK analysis are used to conduct the spatial analysis of Fars province. The data used in this study includes the Fars province road NETWORK extracted from the OSM open source system, as well as the spatial information of the urban and rural areas of Fars province extracted from the website of the Statistics Center of Iran. Regarding research methods, Pandas Library and NETWORKX Library were used in the Python programming platform to form the NETWORK graph and analyze the CENTRALITY indicators, while ARC GIS software was for final processing and visualization of data and information. According to theNETWORK CENTRALITY indices and the location of the province cities, notwithstanding Shiraz as the main hub of development in the province, Zarghan, Sadra, Kavar, Lepui, Khane Zenian and Noorabad are the main development priorities. In addition, the Shiraz-Marvdasht and Shiraz-Khaneh Zenian roads were determined to be the most important transportation paths and communication corridors. The results and findings of this study showed that NETWORK-based modeling and the use of graph-based analytics (in particular, social NETWORK analysis techniques) can be useful and effective as new analytical methods in regional studies and planning.

Background and Objectives: Embedding social NETWORKs has attracted researchers’ attention so far. The aim of NETWORK embedding is to learn a low-dimensional representation of each NETWORK vertex while maintaining the structure and characteristics of the NETWORK. Most of these existing NETWORK embedding methods focus on only preserving the structure of NETWORKs, but they mostly ignore the semantic and CENTRALITY-based information. Moreover, the vertices selection has been done blindly (greedy) in the existing methods.Methods: In this paper, a comprehensive algorithm entitled CSRW stands for CENTRALITY, and a semantic-based random walk is proposed for the NETWORK embedding process based on the main criteria of the CENTRALITY concept as well as the semantic impact of the textual information of each vertex and considering the impact of neighboring nodes. in CSRW, textual analysis based on the BTM topic modelling approach is investigated and the final display is performed using the Skip-Gram model in the NETWORK.Results: The conducted experiments have shown the robustness of the proposed method of this paper in comparison to other existing classical approaches such as DeepWalk, CARE, CONE, COANE, and DCB in terms of vertex classification, and link prediction. And in the criterion of link prediction in a Subgraph with 5000 members, an accuracy of 0.91 has been reached for the criterion of closeness CENTRALITY and is better than other methods.Conclusion: The CSRW algorithm is scalable and has achieved higher accuracy on larger datasets.

Purpose: The aerospace industry and technology are always considered one of the most important and valuable industries due to their special and unique features and applications. The field of aerospace research is a priority in the grand strategies of science and technology development, and it is essential to focus on it. Aerospace researchers and experts play critical roles in advancing aerospace science and industry. They are responsible for conducting scientific and industrial activities as well as research. Evaluating the research performance and quality of aerospace researchers at the international level is crucial. The current research aims to study scientometrics and analyze the CENTRALITY metrics of the co-authorship NETWORK of aerospace researchers at the international level. This will be done using data available on the Web of Science Core Collection (WOSCC). Methodology: The research conducted is of an applied nature, employing an analytical approach. In this article, the technique of NETWORK analysis has been employed to visualize the NETWORK of co-authorship at both the micro and macro levels. This includes analyzing the social NETWORK of co-authorship among researchers and their organizations, as well as examining CENTRALITY indicators and conducting NETWORK analysis of researchers' research topics. The current research community includes all aerospace researchers, with 153, 994 records indexed on the Web of Science Core Collection (WOSCC) from 1945 to 2021. There are 161, 156 aerospace researchers, of which 6, 706 were anonymous and were excluded from the research population. Therefore, 154, 450 researchers were included in the study. Data Lab was used to accurately extract data for aerospace researchers. Ravar PreMap was also used to standardize data and prepare a square matrix for researchers. The symmetric correlation matrix of researchers (AU) was obtained using Bibexcel and Netdraw. Then, the required centralities were calculated. Co-authorship maps were also created using NetDraw. Co-authorship NETWORK analysis technique was used for data analysis. A 157×157 matrix was considered to identify keywords that appeared with a frequency of 70 or more. This matrix was used to create a NETWORK of commonly researched topics among researchers. VOSviewer version 1. 6. 18 was used to visualize co-authorship NETWORKs. Findings: The density of the co-authorship NETWORK among aerospace researchers is low, and the NETWORK exhibits low cohesion. In the current research, five clusters of collaboration were identified, with the center consisting of prominent researchers in the field of aerospace. "David A. Fulghum" of the Maritime Center in America published 863 articles in the field of aerospace between 1983 and 2003. "Florian Menter" from Ensys Germany has the highest number of citations (excluding self-citations) for published articles in the aerospace field. Out of 87, 778 keywords identified in the Web of Science Core Collection (WOSC C) in aerospace, 9712 were associated with Florian Menter. Additionally, a map was prepared using 157 keywords that had a frequency of 70 or more. The co-word clusters of the aerospace NETWORK consist of seven topic clusters, 157 nodes, 2679 edges, and have a density of 0. 11. The first cluster was a hot topic discussed in the aerospace industry, and the most frequently mentioned topic, "Aircraft, " is associated with cluster 3. The most prominent topics are aerodynamics, flight control, and vibrations. The most significant scientific collaboration of aerospace researchers is between Giovanni Mengali and Alessandro A. Quarta from the University of Pisa, Italy. The most scientific advancements in aerospace research have been published in the fields of aerodynamics, flight mechanics, control, and vibrations. After the United States, China had the most scholarly communications with other countries. Conclusion: Developing science policy and advancing strategic plans and programs for aerospace research require comprehensive and accurate information about researchers' potential scientific and technical abilities. The involvement of prominent aerospace researchers in communication and scientific collaborations has resulted in the establishment of significant international partnerships in the aerospace industry. In order to effectively participate in robust and cohesive scientific collaboration NETWORKs, it is necessary to enhance communication among researchers, research centers, and countries, and leverage their synergistic capabilities. The present research results are utilized in the science, technology, and innovation policies of the aerospace industry. It is also used in the planning and direction of applied research, as well as in the application of research conducted by aerospace scientific associations, universities, research institutes, and aerospace industry organizations. Additionally, the results obtained from this research can be used to expand international cooperation among aerospace researchers. Another application of the results presented in this article is the optimal utilization of experts and meticulous planning for the establishment and growth of specialized clusters of aerospace researchers. Prominent aerospace researchers have facilitated the establishment of scientific collaborations and significant partnerships at the international level. Nevertheless, in order to establish stronger and more cohesive scientific collaboration NETWORKs, it is essential to prioritize the exploration of potential connections among researchers, research centers, universities, and countries, as well as their synergistic capabilities.

The dynamic among farmer institutions has essential problems to be addressed, especially regarding the pattern and process of communication interactions developing farmer institutions. Therefore, an assembly of agribusiness information within the communication NETWORK of the farmer group is of primary interest for our study. This study aims to analyze the agribusiness NETWORK structure of beef cattle farmer groups in Subang Regency, West Java, Indonesia. The Social NETWORK Analysis (SNA) used for discovering communication NETWORK structure. Data was collected through interviews using a questionnaire. The census method was used for the sampling technique and UCINET 6 used to analyze the data. The results of the study show: 1) The degree CENTRALITY and net draw illustrate the head of farmer groups still plays a role as a source of information for their members even if members can access 1-3 other sources, 2) The closeness CENTRALITY average is still high and approaching its maximum. The limitation of this study is that only in quantitative approach. Therefore, it is recommended to conduct further research in a qualitative approach to further analyze the roles play in the NETWORKs that can be considered in increasing group social capital.

Stock price and its changes which reflect the individuals’ investment decisions in economic environment are the most important factors in evaluating the economic value of a company in stock market. Stock price changes are not independent of each other. Therefore, study of the correlation between stock price changes provides a better understanding of market performance for investors. Analysis of the stock market based on complex NETWORKs makes it possible to study the correlation of stock prices. In this paper, using the stock market data in Tehran Stock Exchange, Iran's stock market NETWORK is created by the threshold method, then the structural characteristics of the NETWORK are examined and the stock CENTRALITY is calculated. By examining the criterion of the stock CENTRALITY in the NETWORK and ranking industries based on it, results show that the industries producing chemical products with a relatively higher value added ratio have the highest degree of CENTRALITY among other industries. In addition, major economic sectors with more growth are relatively more centralized. Hence, if the industry has more growth, the relationship between its stocks may be stronger and more centralized. In other words, sectoral growth in Iran economy can reflect in CENTRALITY or relationship between stocks in that sector. These analyses provide a more in-depth insight into the structure of the stock market.