Seung-won Hwang
- Information Systems top 2%
- Computer Networks and Communications top 5%
- Artificial Intelligence top 5%
- Signal Processing top 2%
- Computer Vision and Pattern Recognition top 10%
- Co-authors
- Jongwuk LeeGae-won YouJin-Han KimSunghun KimSang-Hoon LeeKevin Chen–Chuan ChangSameh ElniketyYuxiong He
- Topics
- Topic Modeling (30 papers)Natural Language Processing Techniques (24 papers)Data Management and Algorithms (19 papers)
- Partner nations
- South KoreaUnited StatesChina
In The Last Decade
Seung-won Hwang
67 papers receiving 721 citations
Peers
Comparison fields: 5 of 50
- Information Systems 342
- Computer Networks and Communications 313
- Artificial Intelligence 300
- Signal Processing 272
- Computer Vision and Pattern Recognition 98
Countries citing papers authored by Seung-won Hwang
This map shows the geographic impact of Seung-won Hwang'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 Seung-won Hwang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Seung-won Hwang more than expected).
Fields of papers citing papers by Seung-won Hwang
This network shows the impact of papers produced by Seung-won Hwang. 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 Seung-won Hwang. The network helps show where Seung-won Hwang may publish in the future.
Co-authorship network of co-authors of Seung-won Hwang
This figure shows the co-authorship network connecting the top 25 collaborators of Seung-won Hwang. A scholar is included among the top collaborators of Seung-won Hwang 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 Seung-won Hwang. Seung-won Hwang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 3 | |
| 7 | 0 | |
| 8 | 0 | |
| 9 | 2 | |
| 10 | 7 | |
| 11 | 2 | |
| 12 | 3 | |
| 13 | 0 | |
| 14 | 4 | |
| 15 | 10 | |
| 16 | Enriching Entity Translation Discovery using Selective Temporality | 4 |
| 17 | Bootstrapping Entity Translation on Weakly Comparable Corpora | 11 |
| 18 | 8 | |
| 19 | Mining Name Translations from Entity Graph Mapping | 13 |
| 20 | 1 |
About Seung-won Hwang
Seung-won Hwang is a scholar working on Signal Processing, Artificial Intelligence and Software, having authored 72 papers that have together received 742 indexed citations. Recurring topics across this work include Topic Modeling (30 papers), Natural Language Processing Techniques (24 papers) and Data Management and Algorithms (19 papers). The work is most often cited by research in Signal Processing (272 citations), Information Systems (342 citations) and Computer Networks and Communications (313 citations). Seung-won Hwang has collaborated with scholars based in South Korea, United States and China. Frequent co-authors include Jongwuk Lee, Gae-won You, Jin-Han Kim, Sunghun Kim, Sang-Hoon Lee, Kevin Chen–Chuan Chang, Sameh Elnikety, Yuxiong He, Saehoon Kim and Sunghun Kim. Their work appears in journals such as Scientific Reports, Information Sciences and IEEE Transactions on Knowledge and Data Engineering.
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.