Jingchao Ni
- Artificial Intelligence top 1%
- Computer Networks and Communications top 5%
- Signal Processing top 2%
- Statistical and Nonlinear Physics top 5%
- Information Systems top 5%
- Co-authors
- Wei ChengBo ZongHaifeng ChenDongjin SongYuncong ChenCristian LumezanuNitesh V. ChawlaChuxu Zhang
- Topics
- Complex Network Analysis Techniques (13 papers)Advanced Graph Neural Networks (13 papers)Anomaly Detection Techniques and Applications (9 papers)
- Journals
- BMC BioinformaticsIEEE Transactions on Knowledge and Data EngineeringKnowledge and Information Systems
- Partner nations
- United StatesChinaJapan
In The Last Decade
Jingchao Ni
36 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 90
- Artificial Intelligence 985
- Computer Networks and Communications 380
- Signal Processing 362
- Statistical and Nonlinear Physics 216
- Information Systems 167
Countries citing papers authored by Jingchao Ni
This map shows the geographic impact of Jingchao Ni'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 Jingchao Ni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jingchao Ni more than expected).
Fields of papers citing papers by Jingchao Ni
This network shows the impact of papers produced by Jingchao Ni. 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 Jingchao Ni. The network helps show where Jingchao Ni may publish in the future.
Co-authorship network of co-authors of Jingchao Ni
This figure shows the co-authorship network connecting the top 25 collaborators of Jingchao Ni. A scholar is included among the top collaborators of Jingchao Ni 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 Jingchao Ni. Jingchao Ni 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 | 4 | |
| 3 | 12 | |
| 4 | 47 | |
| 5 | 1 | |
| 6 | 7 | |
| 7 | Learning to Drop: Robust Graph Neural Network via Topological Denoisingbreakdown → | 156 |
| 8 | 1 | |
| 9 | Inductive and Unsupervised Representation Learning on Graph Structured Objects | 6 |
| 10 | 8 | |
| 11 | A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Databreakdown → | 482 |
| 12 | 42 | |
| 13 | 11 | |
| 14 | 23 | |
| 15 | 5 | |
| 16 | 4 | |
| 17 | 6 | |
| 18 | 7 | |
| 19 | 24 | |
| 20 | 44 |
About Jingchao Ni
Jingchao Ni is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Signal Processing, having authored 37 papers that have together received 1.3k indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (13 papers), Advanced Graph Neural Networks (13 papers) and Anomaly Detection Techniques and Applications (9 papers). The work is most often cited by research in Signal Processing (362 citations), Artificial Intelligence (985 citations) and Statistical and Nonlinear Physics (216 citations). Jingchao Ni has collaborated with scholars based in United States, China and Japan. Frequent co-authors include Wei Cheng, Bo Zong, Haifeng Chen, Dongjin Song, Yuncong Chen, Cristian Lumezanu, Nitesh V. Chawla, Chuxu Zhang, Xinyang Feng and X. D. Zhang. Their work appears in journals such as BMC Bioinformatics, IEEE Transactions on Knowledge and Data Engineering and Knowledge and Information Systems.
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.