Dongjin Song

137 total papers · 6.0k total citations
62 papers, 2.5k citations indexed

About

Dongjin Song is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Dongjin Song has authored 62 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Artificial Intelligence, 15 papers in Computer Vision and Pattern Recognition and 13 papers in Signal Processing. Recurrent topics in Dongjin Song's work include Advanced Graph Neural Networks (14 papers), Domain Adaptation and Few-Shot Learning (12 papers) and Time Series Analysis and Forecasting (12 papers). Dongjin Song is often cited by papers focused on Advanced Graph Neural Networks (14 papers), Domain Adaptation and Few-Shot Learning (12 papers) and Time Series Analysis and Forecasting (12 papers). Dongjin Song collaborates with scholars based in United States, China and Australia. Dongjin Song's co-authors include Nitesh V. Chawla, Chuxu Zhang, Chao Huang, Ananthram Swami, Bo Zong, Dacheng Tao, David Meyer, Wei Cheng, Haifeng Chen and Jingchao Ni and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal of Cleaner Production and IEEE Transactions on Image Processing.

In The Last Decade

Dongjin Song

61 papers receiving 2.4k citations

Hit Papers

Heterogeneous Graph Neura... 2019 2026 2021 2023 2019 2019 2024 250 500 750

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Dongjin Song 1.5k 518 418 401 386 62 2.5k
Chuxu Zhang 2.0k 1.3× 411 0.8× 490 1.2× 792 2.0× 340 0.9× 79 3.1k
Wei Cheng 1.8k 1.2× 327 0.6× 720 1.7× 233 0.6× 543 1.4× 87 2.4k
Bryan Hooi 1.9k 1.3× 369 0.7× 813 1.9× 596 1.5× 636 1.6× 96 2.8k
Eduardo R. Hruschka 2.4k 1.6× 641 1.2× 211 0.5× 707 1.8× 523 1.4× 80 3.2k
Yao Ma 2.3k 1.5× 601 1.2× 344 0.8× 1.1k 2.8× 239 0.6× 75 3.3k
Minnan Luo 1.5k 1.0× 991 1.9× 354 0.8× 375 0.9× 170 0.4× 95 2.4k
Shaojie Qiao 777 0.5× 361 0.7× 288 0.7× 363 0.9× 294 0.8× 142 2.2k
Yu Xie 1.8k 1.2× 370 0.7× 356 0.9× 376 0.9× 98 0.3× 87 2.4k
Emmanuel Müller 1.9k 1.2× 449 0.9× 557 1.3× 340 0.8× 525 1.4× 79 2.3k
Zahid Halim 843 0.6× 351 0.7× 512 1.2× 409 1.0× 262 0.7× 118 2.2k

Countries citing papers authored by Dongjin Song

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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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.

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