Weicong Ding
- General Social Sciences top 1%
- Computational and Text Analysis Methods 2
- Communication top 10%
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- Topic Modeling 5
- Advanced Text Analysis Techniques 2
- Advanced Graph Neural Networks 2
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- Complex Network Analysis Techniques 3
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- Data Management and Algorithms 3
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- Recommender Systems and Techniques 2
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- Consumer Market Behavior and Pricing 2
- Co-authors
- Prakash IshwarЛэй ГуоChris J. VargoZixuan PanAzin AshkanBrian ErikssonVenkatesh SaligramaMohammad Hossein Rohban
- Journals
- IEEE Transactions on Signal and Information Processing over Networks (1 paper)IEEE Journal of Selected Topics in Signal Processing (1 paper)Journalism & Mass Communication Quarterly (1 paper)
- Partner nations
- United StatesSwitzerland
In The Last Decade
Weicong Ding
13 papers receiving 249 citations
Hit Papers
Peers
Comparison fields: 5 of 66
- General Social Sciences 53
- Communication 71
- Artificial Intelligence 82
- Statistical and Nonlinear Physics 29
- Sociology and Political Science 95
Countries citing papers authored by Weicong Ding
This map shows the geographic impact of Weicong Ding'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 Weicong Ding with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Weicong Ding more than expected).
Fields of papers citing papers by Weicong Ding
This network shows the impact of papers produced by Weicong Ding. 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 Weicong Ding. The network helps show where Weicong Ding may publish in the future.
Co-authorship network
The 14 scholars most cited alongside Weicong Ding, 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 | 2019 | 7 | |
| 2 | 2018 | 15 | |
| 3 | Discovery of Evolving Semantics through Dynamic Word Embedding Learning. | 2017 | 3 |
| 4 | 2017 | 1 | |
| 5 | 2017 | 10 | |
| 6 | Big Social Data Analytics in Journalism and Mass Communicationbreakdown → | 2016 | 158 |
| 7 | 2016 | 1 | |
| 8 | 2016 | 39 | |
| 9 | A Topic Modeling Approach to Ranking | 2015 | 4 |
| 10 | 2015 | 5 | |
| 11 | Efficient Distributed Topic Modeling with Provable Guarantees | 2014 | 4 |
| 12 | 2013 | 17 | |
| 13 | 2013 | 1 |
About Weicong Ding
Weicong Ding is a scholar working on General Social Sciences, Signal Processing and Artificial Intelligence, having authored 13 papers that have together received 265 indexed citations. Recurring topics across this work include Topic Modeling (5 papers), Complex Network Analysis Techniques (3 papers), Data Management and Algorithms (3 papers), Computational and Text Analysis Methods (2 papers), Recommender Systems and Techniques (2 papers), Advanced Text Analysis Techniques (2 papers), Advanced Graph Neural Networks (2 papers) and Consumer Market Behavior and Pricing (2 papers). The work is most often cited by research in General Social Sciences (53 citations), Communication (71 citations) and Artificial Intelligence (82 citations). Weicong Ding has collaborated with scholars based in United States and Switzerland. Frequent co-authors include Prakash Ishwar, Лэй Гуо, Chris J. Vargo, Zixuan Pan, Azin Ashkan, Brian Eriksson, Venkatesh Saligrama, Mohammad Hossein Rohban, Chandan K. Reddy and Nikhil Rao. Their work appears in journals such as IEEE Transactions on Signal and Information Processing over Networks, IEEE Journal of Selected Topics in Signal Processing, Journalism & Mass Communication Quarterly, Infoscience (Ecole Polytechnique Fédérale de Lausanne) and arXiv (Cornell University).
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.