Xiubo Geng
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Information Systems top 5%
- Signal Processing top 10%
- Management Science and Operations Research top 10%
- Topics
- Topic Modeling (26 papers)Natural Language Processing Techniques (19 papers)Multimodal Machine Learning Applications (14 papers)
- Journals
- IEEE Transactions on Neural Networks and Learning SystemsInformation Processing & ManagementACM Transactions on Intelligent Systems and Technology
- Partner nations
- ChinaAustraliaUnited States
In The Last Decade
Xiubo Geng
39 papers receiving 760 citations
Peers
Comparison fields: 5 of 90
- Artificial Intelligence 545
- Computer Vision and Pattern Recognition 276
- Information Systems 222
- Signal Processing 60
- Management Science and Operations Research 45
Countries citing papers authored by Xiubo Geng
This map shows the geographic impact of Xiubo Geng'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 Xiubo Geng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiubo Geng more than expected).
Fields of papers citing papers by Xiubo Geng
This network shows the impact of papers produced by Xiubo Geng. 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 Xiubo Geng. The network helps show where Xiubo Geng may publish in the future.
Co-authorship network of co-authors of Xiubo Geng
This figure shows the co-authorship network connecting the top 25 collaborators of Xiubo Geng. A scholar is included among the top collaborators of Xiubo Geng 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 Xiubo Geng. Xiubo Geng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 2 | |
| 3 | 2 | |
| 4 | 8 | |
| 5 | 0 | |
| 6 | 7 | |
| 7 | 24 | |
| 8 | 14 | |
| 9 | 7 | |
| 10 | 41 | |
| 11 | 10 | |
| 12 | 3 | |
| 13 | 25 | |
| 14 | ChemistryQA: A Complex Question Answering Dataset from Chemistry | 1 |
| 15 | 0 | |
| 16 | 54 | |
| 17 | 6 | |
| 18 | 1 | |
| 19 | 6 | |
| 20 | A New Probabilistic Model for Rank Aggregation | 52 |
About Xiubo Geng
Xiubo Geng is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems, having authored 42 papers that have together received 798 indexed citations. Recurring topics across this work include Topic Modeling (26 papers), Natural Language Processing Techniques (19 papers) and Multimodal Machine Learning Applications (14 papers). The work is most often cited by research in Artificial Intelligence (545 citations), Computer Vision and Pattern Recognition (276 citations) and Information Systems (222 citations). Xiubo Geng has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Tao Qin, Tie‐Yan Liu, Hang Li, Daxin Jiang, Tao Shen, Xudong Zhang, Wenkui Ding, Heung‐Yeung Shum, Andrew O. Arnold and Guodong Long. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, Information Processing & Management and ACM Transactions on Intelligent Systems and Technology.
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.