Dandan Song

1.7k total citations
82 papers, 1.0k citations indexed

About

Dandan Song is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Dandan Song has authored 82 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Artificial Intelligence, 19 papers in Information Systems and 13 papers in Computer Vision and Pattern Recognition. Recurrent topics in Dandan Song's work include Topic Modeling (27 papers), Text and Document Classification Technologies (14 papers) and Natural Language Processing Techniques (12 papers). Dandan Song is often cited by papers focused on Topic Modeling (27 papers), Text and Document Classification Technologies (14 papers) and Natural Language Processing Techniques (12 papers). Dandan Song collaborates with scholars based in China, United States and Singapore. Dandan Song's co-authors include Lejian Liao, Yang Fang, Kunjie Fan, William K. Cheung, Jing He, Xin Li, Shouyi Yin, Peng Ouyang, Shaojun Wei and Leibo Liu and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and NeuroImage.

In The Last Decade

Dandan Song

77 papers receiving 985 citations

Peers

Dandan Song
Comparison fields: 5 of 103
  • Artificial Intelligence 503
  • Information Systems 279
  • Molecular Biology 216
  • Computer Vision and Pattern Recognition 166
  • Signal Processing 113
Replace Hongyun Cai with:
Hongyun Cai Singapore
Jaime G. Carbonell United States
Hayato Yamana Japan
Marion Neumann Germany
John Boaz Lee United States
Shikun Feng China
Saeed Salem United States
Xiaofeng Ding China
Dongxiao He China
Haixuan Yang Ireland
Hongyun Cai Singapore View profile →
Citations per field, relative to Dandan Song
Dandan Song · 1×
Citations per year, relative to Dandan Song
Dandan Song · 1×

Countries citing papers authored by Dandan Song

Since Specialization
Citations

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

Fields of papers citing papers by Dandan Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dandan Song. 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 Dandan Song. The network helps show where Dandan Song may publish in the future.

Co-authorship network of co-authors of Dandan Song

This figure shows the co-authorship network connecting the top 25 collaborators of Dandan Song. A scholar is included among the top collaborators of Dandan Song 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 Dandan Song. Dandan Song 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
# Work Indexed citations
1 3
2 0
3 2
4 5
5 1
6 3
7 9
8 40
9 5
10 34
11 2
12 21
13
A Bilingual Lexicon-Based Multi-class SemanticOrientation Analysis for Microblogs
3
14 28
15
Topic modeling with document relative similarities
14
16
BIT and Purdue at TREC-KBA-CCR Track 2014
1
17
BIT and MSRA at TREC KBA CCR Track 2013
14
18 2
19 0
20
Neural network approach to predict RNA secondary structures
1

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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026