Shou-De Lin

3.1k total citations
137 papers, 1.8k citations indexed

About

Shou-De Lin is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Information Systems. According to data from OpenAlex, Shou-De Lin has authored 137 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 76 papers in Artificial Intelligence, 40 papers in Statistical and Nonlinear Physics and 30 papers in Information Systems. Recurrent topics in Shou-De Lin's work include Complex Network Analysis Techniques (40 papers), Opinion Dynamics and Social Influence (24 papers) and Topic Modeling (19 papers). Shou-De Lin is often cited by papers focused on Complex Network Analysis Techniques (40 papers), Opinion Dynamics and Social Influence (24 papers) and Topic Modeling (19 papers). Shou-De Lin collaborates with scholars based in Taiwan, United States and China. Shou-De Lin's co-authors include Cheng–Te Li, Hsun-Ping Hsieh, Yu Zheng, Hans Chalupsky, Hung-Yi Lo, Tsung-Ting Kuo, Mi-Yen Yeh, Man-Kwan Shan, Hsin‐Min Wang and Chung‐Yi Li and has published in prestigious journals such as PLoS ONE, Information Sciences and Artificial Intelligence.

In The Last Decade

Shou-De Lin

132 papers receiving 1.7k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Shou-De Lin Taiwan 24 983 498 394 259 229 137 1.8k
Hongbo Deng China 23 901 0.9× 915 1.8× 387 1.0× 296 1.1× 219 1.0× 78 1.8k
Sen Wu China 19 1.3k 1.3× 535 1.1× 368 0.9× 279 1.1× 178 0.8× 81 2.0k
Xiangfeng Luo China 25 1.5k 1.5× 589 1.2× 289 0.7× 511 2.0× 303 1.3× 246 2.5k
Tomoharu Iwata Japan 22 1.0k 1.0× 487 1.0× 312 0.8× 379 1.5× 185 0.8× 135 2.0k
Martin Atzmueller Germany 18 505 0.5× 311 0.6× 378 1.0× 167 0.6× 224 1.0× 143 1.3k
Zhaohui Zheng United States 22 1.3k 1.3× 1.2k 2.3× 237 0.6× 463 1.8× 275 1.2× 55 2.4k
Pasquale De Meo Italy 26 755 0.8× 652 1.3× 658 1.7× 163 0.6× 439 1.9× 95 2.0k
Yong Ge United States 22 643 0.7× 541 1.1× 130 0.3× 304 1.2× 225 1.0× 63 1.6k
Ni Lao United States 19 1.9k 1.9× 520 1.0× 193 0.5× 335 1.3× 292 1.3× 42 2.5k
Yao Wu China 17 792 0.8× 830 1.7× 252 0.6× 315 1.2× 156 0.7× 42 1.6k

Countries citing papers authored by Shou-De Lin

Since Specialization
Citations

This map shows the geographic impact of Shou-De Lin'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 Shou-De Lin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shou-De Lin more than expected).

Fields of papers citing papers by Shou-De Lin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shou-De Lin. 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 Shou-De Lin. The network helps show where Shou-De Lin may publish in the future.

Co-authorship network of co-authors of Shou-De Lin

This figure shows the co-authorship network connecting the top 25 collaborators of Shou-De Lin. A scholar is included among the top collaborators of Shou-De Lin 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 Shou-De Lin. Shou-De Lin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Li, Cheng–Te, et al.. (2025). Learning on Missing Tabular Data: Attention with Self-Supervision, Not Imputation, is All You Need. ACM Transactions on Intelligent Systems and Technology. 1 indexed citations
2.
Li, Cheng–Te, et al.. (2024). Unilateral boundary time series forecasting. Frontiers in Big Data. 7. 1376023–1376023.
3.
Sun, Fan-Yun, et al.. (2019). A Regulation Enforcement Solution for Multi-agent Reinforcement Learning. arXiv (Cornell University). 2201–2203. 1 indexed citations
4.
Yen, Ian En-Hsu, et al.. (2018). MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization. Neural Information Processing Systems. 31. 10868–10876. 1 indexed citations
5.
Yeh, Mi-Yen, et al.. (2017). Preserving Proximity and Global Ranking for Node Embedding. Neural Information Processing Systems. 5168–5177. 4 indexed citations
6.
Yeh, Mi-Yen, et al.. (2017). PRUNE: Preserving Proximity and Global Ranking for Network Embedding. Neural Information Processing Systems. 30. 5257–5266. 31 indexed citations
7.
Hsieh, Hsun-Ping, Cheng–Te Li, & Shou-De Lin. (2015). Measuring and recommending time-sensitive routes from location-based data. International Conference on Artificial Intelligence. 4193–4196. 2 indexed citations
8.
Yen, Ian En-Hsu, et al.. (2015). A dual-augmented block minimization framework for learning with limited memory. Neural Information Processing Systems. 28. 3582–3590. 2 indexed citations
9.
Lin, Shou-De, et al.. (2014). Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space. Neural Information Processing Systems. 27. 2456–2464. 23 indexed citations
10.
Jiang, Han, et al.. (2013). IJCAI 2013, Proceedings of the 23rd International Joint Conference on Artificial Intelligence. National Conference on Artificial Intelligence. 53 indexed citations
11.
Yan, Rui, Han Jiang, Mirella Lapata, et al.. (2013). Semantic v.s. Positions: Utilizing Balanced Proximity in Language Model Smoothing for Information Retrieval. Edinburgh Research Explorer (University of Edinburgh). 507–515. 3 indexed citations
12.
Chiang, Tsung‐Hsien, Hung-Yi Lo, & Shou-De Lin. (2012). A Ranking-based KNN Approach for Multi-Label Classification. Asian Conference on Machine Learning. 81–96. 26 indexed citations
13.
Yuan, Hsiao‐Wei, Cho‐ying Huang, Chung‐Ho Wang, et al.. (2012). Regional Scale High Resolution δ18O Prediction in Precipitation Using MODIS EVI. PLoS ONE. 7(9). e45496–e45496. 3 indexed citations
14.
Hsieh, Hsun-Ping, Cheng–Te Li, & Shou-De Lin. (2012). Exploiting large-scale check-in data to recommend time-sensitive routes. 55–62. 62 indexed citations
15.
Kuo, Tsung-Ting, et al.. (2012). Exploiting Latent Information to Predict Diffusions of Novel Topics on Social Networks. Meeting of the Association for Computational Linguistics. 344–348. 11 indexed citations
16.
Li, Cheng–Te, et al.. (2011). MemeTube: A Sentiment-based Audiovisual System for Analyzing and Displaying Microblog Messages. Meeting of the Association for Computational Linguistics. 32–37. 8 indexed citations
17.
Yu, Hsiang‐Fu, Hung-Yi Lo, Hsun-Ping Hsieh, et al.. (2010). Feature Engineering and Classifier Ensemble for KDD Cup 2010. Knowledge Discovery and Data Mining. 89 indexed citations
18.
Lo, Hung-Yi, Kai‐Wei Chang, Shang-Tse Chen, et al.. (2009). An ensemble of three classifiers for KDD cup 2009: expanded linear model, heterogeneous boosting, and selective naïve Bayes. Knowledge Discovery and Data Mining. 57–64. 11 indexed citations
19.
Lin, Shou-De & Craig A. Knoblock. (2005). SERGEANT: A framework for building more flexible web agents by exploiting a search engine. Web Intelligence and Agent Systems An International Journal. 3(1). 1–15. 2 indexed citations
20.
Lin, Shou-De. (2004). Interesting instance discovery in multi-relational data. National Conference on Artificial Intelligence. 991–992. 3 indexed citations

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.

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