Chang Zhou
- Artificial Intelligence top 0.5%
- Information Systems top 0.5%
- Computer Vision and Pattern Recognition top 1%
- Computer Networks and Communications top 5%
- Management Science and Operations Research top 2%
- Topics
- Advanced Graph Neural Networks (15 papers)Recommender Systems and Techniques (14 papers)Graph Theory and Algorithms (9 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Chang Zhou
49 papers receiving 2.1k citations
Hit Papers
Peers
Comparison fields: 5 of 107
- Artificial Intelligence 1.4k
- Information Systems 1.3k
- Computer Vision and Pattern Recognition 769
- Computer Networks and Communications 282
- Management Science and Operations Research 272
Countries citing papers authored by Chang Zhou
This map shows the geographic impact of Chang Zhou'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 Chang Zhou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chang Zhou more than expected).
Fields of papers citing papers by Chang Zhou
This network shows the impact of papers produced by Chang Zhou. 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 Chang Zhou. The network helps show where Chang Zhou may publish in the future.
Co-authorship network of co-authors of Chang Zhou
This figure shows the co-authorship network connecting the top 25 collaborators of Chang Zhou. A scholar is included among the top collaborators of Chang Zhou 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 Chang Zhou. Chang Zhou 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 | 0 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 13 | |
| 6 | 2 | |
| 7 | 0 | |
| 8 | 0 | |
| 9 | 8 | |
| 10 | 4 | |
| 11 | 9 | |
| 12 | 40 | |
| 13 | Exploring Sparse Expert Models and Beyond | 4 |
| 14 | Contrastive Learning for Debiased Candidate Generation at Scale. | 1 |
| 15 | CogLTX: Applying BERT to Long Texts | 48 |
| 16 | ExperienceThinking: Hyperparameter Optimization with Budget Constraints | 2 |
| 17 | 179 | |
| 18 | Deep Interest Evolution Network for Click-Through Rate Predictionbreakdown → | 578 |
| 19 | 46 | |
| 20 | 3 |
About Chang Zhou
Chang Zhou is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Information Systems, having authored 53 papers that have together received 2.2k indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (15 papers), Recommender Systems and Techniques (14 papers) and Graph Theory and Algorithms (9 papers). The work is most often cited by research in Information Systems (1.3k citations), Artificial Intelligence (1.4k citations) and Computer Vision and Pattern Recognition (769 citations). Chang Zhou has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Hongxia Yang, Jie Tang, Na Mou, Guorui Zhou, Weijie Bian, Ying Fan, Xiaoqiang Zhu, Kun Gai, Jingren Zhou and Xiaofei Liu. Their work appears in journals such as Bioinformatics, International Journal of Molecular Sciences and IEEE Access.
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.