Countries where authors publish in Stochastic Models
Since Specialization
Citations
This map shows the geographic impact of research published in Stochastic Models. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by papers published in Stochastic Models with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stochastic Models more than expected).
This network shows the impact of papers published in Stochastic Models. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Stochastic Models.
About Stochastic Models
The 577 papers published in Stochastic Models in the last decades have received a total of 5.1k indexed citations . Papers published in Stochastic Models usually cover Management Information Systems (250 papers), Statistics and Probability (155 papers), Management Science and Operations Research (223 papers), Finance (141 papers) and Mathematical Physics (105 papers) specifically the topics of Advanced Queuing Theory Analysis (250 papers), Probability and Risk Models (175 papers), Stochastic processes and financial applications (107 papers), Stochastic processes and statistical mechanics (99 papers), Financial Risk and Volatility Modeling (62 papers), Markov Chains and Monte Carlo Methods (51 papers), Statistical Distribution Estimation and Applications (49 papers) and Network Traffic and Congestion Control (47 papers). The most active scholars publishing in Stochastic Models are Soohan Ahn, V. Ramaswami, Qihe Tang, Sidney I. Resnick, Marcin Magdziarz, Miklós Telek, Nigel Bean, Kam Chuen Yuen, Yiqing Chen and Amaury Lambert.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.