Dongjin Song
- Artificial Intelligence top 0.5%
- Computer Vision and Pattern Recognition top 2%
- Computer Networks and Communications top 5%
- Information Systems top 2%
- Signal Processing top 2%
- Co-authors
- Nitesh V. ChawlaChuxu ZhangAnanthram SwamiChao HuangBo ZongDacheng TaoDavid MeyerWei Cheng
- Topics
- Advanced Graph Neural Networks (15 papers)Domain Adaptation and Few-Shot Learning (12 papers)Time Series Analysis and Forecasting (12 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceJournal of Cleaner ProductionIEEE Transactions on Image Processing
- Partner nations
- United StatesChinaAustralia
In The Last Decade
Dongjin Song
61 papers receiving 2.5k citations
Hit Papers
Peers
Comparison fields: 5 of 143
- Artificial Intelligence 1.6k
- Computer Vision and Pattern Recognition 522
- Computer Networks and Communications 425
- Information Systems 409
- Signal Processing 396
Countries citing papers authored by Dongjin Song
This map shows the geographic impact of Dongjin 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 Dongjin Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dongjin Song more than expected).
Fields of papers citing papers by Dongjin Song
This network shows the impact of papers produced by Dongjin 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 Dongjin Song. The network helps show where Dongjin Song may publish in the future.
Co-authorship network of co-authors of Dongjin Song
This figure shows the co-authorship network connecting the top 25 collaborators of Dongjin Song. A scholar is included among the top collaborators of Dongjin 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 Dongjin Song. Dongjin 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 | 1 | |
| 3 | 4 | |
| 4 | 0 | |
| 5 | 2 | |
| 6 | 7 | |
| 7 | 1 | |
| 8 | Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospectsbreakdown → | 83 |
| 9 | 4 | |
| 10 | 2 | |
| 11 | 3 | |
| 12 | 3 | |
| 13 | 21 | |
| 14 | 23 | |
| 15 | Inductive and Unsupervised Representation Learning on Graph Structured Objects | 6 |
| 16 | Fast structural binary coding | 19 |
| 17 | 3 | |
| 18 | 166 | |
| 19 | 5 | |
| 20 | Localized Properties in Flakeboard: A Simulation Using Stacked Flakes | 3 |
About Dongjin Song
Dongjin Song is a scholar working on Artificial Intelligence, Signal Processing and Applied Psychology, having authored 67 papers that have together received 2.6k indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (15 papers), Domain Adaptation and Few-Shot Learning (12 papers) and Time Series Analysis and Forecasting (12 papers). The work is most often cited by research in Artificial Intelligence (1.6k citations), Signal Processing (396 citations) and Statistical and Nonlinear Physics (388 citations). Dongjin Song has collaborated with scholars based in United States, China and Australia. Frequent co-authors include Nitesh V. Chawla, Chuxu Zhang, Ananthram Swami, Chao Huang, Bo Zong, Dacheng Tao, David Meyer, Wei Cheng, Jingchao Ni and Haifeng Chen. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal of Cleaner Production and IEEE Transactions on Image Processing.
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.