Nathan N. Liu
- Information Systems top 0.5%
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 2%
- Management Science and Operations Research top 2%
- Computer Networks and Communications top 5%
- Co-authors
- Qiang YangBin CaoRajan M. LukoseY. ZhouMartin ScholzRong PanEvan Wei XiangMin Zhao
- Topics
- Recommender Systems and Techniques (11 papers)Complex Network Analysis Techniques (4 papers)Advanced Graph Neural Networks (4 papers)
- Journals
- ACM Transactions on Intelligent Systems and TechnologyRare & Special e-Zone (The Hong Kong University of Science and Technology)
- Partner nations
- Hong KongChinaUnited States
In The Last Decade
Nathan N. Liu
12 papers receiving 1.5k citations
Hit Papers
Peers
Comparison fields: 5 of 61
- Information Systems 1.3k
- Artificial Intelligence 803
- Computer Vision and Pattern Recognition 378
- Management Science and Operations Research 330
- Computer Networks and Communications 184
Countries citing papers authored by Nathan N. Liu
This map shows the geographic impact of Nathan N. Liu'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 Nathan N. Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nathan N. Liu more than expected).
Fields of papers citing papers by Nathan N. Liu
This network shows the impact of papers produced by Nathan N. Liu. 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 Nathan N. Liu. The network helps show where Nathan N. Liu may publish in the future.
Co-authorship network of co-authors of Nathan N. Liu
This figure shows the co-authorship network connecting the top 25 collaborators of Nathan N. Liu. A scholar is included among the top collaborators of Nathan N. Liu 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 Nathan N. Liu. Nathan N. Liu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 61 | |
| 2 | 88 | |
| 3 | 154 | |
| 4 | 60 | |
| 5 | Transfer Learning for Collective Link Prediction in Multiple Heterogenous Domains | 83 |
| 6 | 33 | |
| 7 | 84 | |
| 8 | 59 | |
| 9 | 83 | |
| 10 | 1 | |
| 11 | One-Class Collaborative Filteringbreakdown → | 682 |
| 12 | 187 |
About Nathan N. Liu
Nathan N. Liu is a scholar working on Information Systems, Statistical and Nonlinear Physics and Signal Processing, having authored 12 papers that have together received 1.6k indexed citations. Recurring topics across this work include Recommender Systems and Techniques (11 papers), Complex Network Analysis Techniques (4 papers) and Advanced Graph Neural Networks (4 papers). The work is most often cited by research in Information Systems (1.3k citations), Computational Mathematics (26 citations) and Management Science and Operations Research (330 citations). Nathan N. Liu has collaborated with scholars based in Hong Kong, China and United States. Frequent co-authors include Qiang Yang, Bin Cao, Rajan M. Lukose, Y. Zhou, Martin Scholz, Rong Pan, Evan Wei Xiang, Min Zhao, Min Zhao and Ou Jin. Their work appears in journals such as ACM Transactions on Intelligent Systems and Technology and Rare & Special e-Zone (The Hong Kong University of Science 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.