Yang Xiang
- Artificial Intelligence top 1%
- Topic Modeling 20
- Natural Language Processing Techniques 10
- Advanced Graph Neural Networks 7
- Advanced Text Analysis Techniques 6
- Advanced Software Engineering Methodologies 5
- Information Systems top 1%
- Service-Oriented Architecture and Web Services 10
- Recommender Systems and Techniques 9
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- Semiconductor materials and devices 6
Yang Xiang
63 papers receiving 1.5k citations
Hit Papers
Peers
Comparison fields: 5 of 127
- Artificial Intelligence 841
- Information Systems 549
- Management Science and Operations Research 239
- Computer Networks and Communications 201
- Computer Vision and Pattern Recognition 145
Countries citing papers authored by Yang Xiang
This map shows the geographic impact of Yang Xiang'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 Yang Xiang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yang Xiang more than expected).
Fields of papers citing papers by Yang Xiang
This network shows the impact of papers produced by Yang Xiang. 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 Yang Xiang. The network helps show where Yang Xiang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Yang Xiang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 1 | |
| 4 | 2024 | 0 | |
| 5 | 2023 | 8 | |
| 6 | 2023 | 4 | |
| 7 | 2023 | 15 | |
| 8 | 2023 | 3 | |
| 9 | 2022 | 1 | |
| 10 | 2022 | 4 | |
| 11 | 2020 | 16 | |
| 12 | 2020 | 1 | |
| 13 | Neural Open Relation Extraction via an Overlap-aware Sequence Tagging Scheme | 2019 | 1 |
| 14 | 2018 | 14 | |
| 15 | TTMF: A Triple Trustworthiness Measurement Frame for Knowledge Graphs. | 2018 | 2 |
| 16 | Achieving better outcomes for Chinese law students studying overseas | 2016 | 1 |
| 17 | Survey of Semantics-Based Recommendation Algorithms | 2016 | 1 |
| 18 | Grammatical Error Correction Using Feature Selection and Confidence Tuning | 2013 | 2 |
| 19 | A Mixed Deterministic Model for Coreference Resolution | 2012 | 6 |
| 20 | Study on Composition of Organic Acids in Domestic Beer for Comprehensive Evaluation | 2007 | 3 |
About Yang Xiang
Yang Xiang is a scholar working on Computational Mathematics, Artificial Intelligence and Information Systems, having authored 74 papers that have together received 1.5k indexed citations. Recurring topics across this work include Topic Modeling (20 papers), Natural Language Processing Techniques (10 papers), Service-Oriented Architecture and Web Services (10 papers), Recommender Systems and Techniques (9 papers), Advanced Graph Neural Networks (7 papers), Advanced Text Analysis Techniques (6 papers), Semiconductor materials and devices (6 papers) and Advanced Software Engineering Methodologies (5 papers). The work is most often cited by research in Artificial Intelligence (841 citations), Information Systems (549 citations) and Management Science and Operations Research (239 citations). Yang Xiang has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Shengbin Jia, Xiaojun Chen, Nicholas Jing Yuan, Xing Xie, Zihan Zheng, Zhenhui Li, Guanjie Zheng, Fuzheng Zhang, Guobing Zou and Yixin Chen. Their work appears in journals such as Expert Systems with Applications, Knowledge-Based Systems, Applied Intelligence, Information Sciences and IEEE Transactions on Services Computing.
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.