Xiaotian Han

812 citations
10 papers · 414 indexed · 1 hit paper · h-index 7
Topics
Recommender Systems and Techniques (5 papers)Sentiment Analysis and Opinion Mining (3 papers)Advanced Graph Neural Networks (3 papers)

In The Last Decade

Xiaotian Han

10 papers receiving 403 citations

Hit Papers

Metapath-guided Heterogeneous Graph Neural Network for In...2019202620212023201950100150200

Peers

Xiaotian Han
Comparison fields: 5 of 71
  • Artificial Intelligence 306
  • Information Systems 237
  • Computer Vision and Pattern Recognition 66
  • Statistical and Nonlinear Physics 62
  • Computer Networks and Communications 56
Replace Linmei Hu with:
Linmei Hu China
Yongliang Li China
Kelong Mao China
Tianchi Yang China
Huizhi Liang United Kingdom
İhsan Güneş Türkiye
Suhas Ranganath United States
Chongyang Shi China
Qihao Shi China
Xiaotian Han relative to Linmei Hu China Linmei Hu's profile →
Citations per field
00.5×
Linmei Hu · 1×
Citations per year

Countries citing papers authored by Xiaotian Han

Since Specialization
Citations

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

Fields of papers citing papers by Xiaotian Han

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiaotian Han

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

All Works

10 of 10 papers shown
#WorkIndexed citations
1 1
2 15
3 9
4 10
5 5
6 59
7
Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendationbreakdown →
243
8 50
9 1
10 21

About Xiaotian Han

Xiaotian Han is a scholar working on Artificial Intelligence, Information Systems and Signal Processing, having authored 10 papers that have together received 414 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (5 papers), Sentiment Analysis and Opinion Mining (3 papers) and Advanced Graph Neural Networks (3 papers). The work is most often cited by research in Information Systems (237 citations), Artificial Intelligence (306 citations) and Statistical and Nonlinear Physics (62 citations). Xiaotian Han has collaborated with scholars based in China, United States and Romania. Frequent co-authors include Chuan Shi, Shaohua Fan, Linmei Hu, Yongliang Li, Junxiong Zhu, Philip S. Yu, Senzhang Wang, Song Li, Li Song and Junping Du. Their work appears in journals such as Langmuir, IEEE Transactions on Knowledge and Data Engineering and World Wide Web.

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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