Xin Song

409 citations
25 papers · 251 indexed · h-index 9
Topics
Advanced Graph Neural Networks (4 papers)Online Learning and Analytics (3 papers)Recommender Systems and Techniques (3 papers)
Journals
SHILAP Revista de lepidopterologíaIEEE AccessSustainability
Partner nations
ChinaHong KongAustralia

In The Last Decade

Xin Song

21 papers receiving 244 citations

Peers

Xin Song
Comparison fields: 5 of 77
  • Artificial Intelligence 107
  • Information Systems 48
  • Management Science and Operations Research 34
  • Computer Vision and Pattern Recognition 31
  • Strategy and Management 26
Replace Rahim Ghasemiyeh with:
Rahim Ghasemiyeh Iran
Sreeram Ramakrishnan United States
Rajesh Kumar Yadav India
Hamzah Ali Alkhazaleh United Arab Emirates
Seyedeh Leili Mirtaheri Iran
Navroop Kaur India
Houda Benbrahim Morocco
Kapil Deo Bodha India
Venkata N Inukollu United States
Fares Alharbi Saudi Arabia
Xin Song relative to Rahim Ghasemiyeh Iran Rahim Ghasemiyeh's profile →
Citations per field
00.5×10.8×
Rahim Ghasemiyeh · 1×
Citations per year

Countries citing papers authored by Xin Song

Since Specialization
Citations

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

Fields of papers citing papers by Xin Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xin Song

This figure shows the co-authorship network connecting the top 25 collaborators of Xin Song. A scholar is included among the top collaborators of Xin 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 Xin Song. Xin 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
#WorkIndexed citations
1 0
2 7
3 0
4 8
5 0
6 4
7 6
8 17
9 2
10 3
11 13
12 1
13 13
14 9
15 6
16 96
17 3
18 1
19 3
20 1

About Xin Song

Xin Song is a scholar working on Computer Science Applications, Management Science and Operations Research and Artificial Intelligence, having authored 25 papers that have together received 251 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (4 papers), Online Learning and Analytics (3 papers) and Recommender Systems and Techniques (3 papers). The work is most often cited by research in Artificial Intelligence (107 citations), Management Science and Operations Research (34 citations) and Management Information Systems (21 citations). Xin Song has collaborated with scholars based in China, Hong Kong and Australia. Frequent co-authors include Jing Luan, Zhong Yao, Futao Zhao, Hengshu Zhu, Hui Xiong, Ying Sun, Qing He, Fuzhen Zhuang, Yang Yang and Wenjie Li. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Access and Sustainability.

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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