Yingbo Song
- Computer Networks and Communications top 10%
- Signal Processing top 5%
- Artificial Intelligence top 10%
- Plant Science
- Information Systems top 10%
- Co-authors
- Salvatore J. StolfoAngelos D. KeromytisYanling SongMichael E. LocastoChunyi WangAngelos StavrouHans W. LinderholmFang Wang
- Topics
- Radioactive element chemistry and processing (8 papers)Advanced Malware Detection Techniques (7 papers)Covalent Organic Framework Applications (6 papers)
- Partner nations
- ChinaUnited StatesSweden
In The Last Decade
Yingbo Song
27 papers receiving 458 citations
Hit Papers
Peers
Comparison fields: 5 of 80
- Computer Networks and Communications 158
- Signal Processing 138
- Artificial Intelligence 129
- Plant Science 91
- Information Systems 90
Countries citing papers authored by Yingbo Song
This map shows the geographic impact of Yingbo Song's research. 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 Yingbo Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yingbo Song more than expected).
Fields of papers citing papers by Yingbo Song
This network shows the impact of papers produced by Yingbo Song. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yingbo Song. The network helps show where Yingbo Song may publish in the future.
Co-authorship network of co-authors of Yingbo Song
This figure shows the co-authorship network connecting the top 25 collaborators of Yingbo Song. A scholar is included among the top collaborators of Yingbo Song based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yingbo Song. Yingbo Song is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 1 | |
| 4 | 8 | |
| 5 | 2 | |
| 6 | 5 | |
| 7 | 4 | |
| 8 | 2 | |
| 9 | 13 | |
| 10 | 99 | |
| 11 | 60 | |
| 12 | 7 | |
| 13 | 2 | |
| 14 | 2 | |
| 15 | [Climatic suitability model for spring maize in Northeast China]. | 7 |
| 16 | 5 | |
| 17 | 22 | |
| 18 | 64 | |
| 19 | Density Estimation under Independent Similarly Distributed Sampling Assumptions | 1 |
| 20 | 71 |
About Yingbo Song
Yingbo Song is a scholar working on Signal Processing, Inorganic Chemistry and Industrial and Manufacturing Engineering, having authored 29 papers that have together received 484 indexed citations. Recurring topics across this work include Radioactive element chemistry and processing (8 papers), Advanced Malware Detection Techniques (7 papers) and Covalent Organic Framework Applications (6 papers). The work is most often cited by research in Signal Processing (138 citations), Computer Networks and Communications (158 citations) and Inorganic Chemistry (63 citations). Yingbo Song has collaborated with scholars based in China, United States and Sweden. Frequent co-authors include Salvatore J. Stolfo, Angelos D. Keromytis, Yanling Song, Michael E. Locasto, Chunyi Wang, Angelos Stavrou, Hans W. Linderholm, Fang Wang, Guoyu Ren and Yanju Liu. Their work appears in journals such as Advanced Materials, Advanced Functional Materials and The Science of The Total Environment.
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.