Xiangchen Song

519 total citations
13 papers, 173 citations indexed

About

Xiangchen Song is a scholar working on Artificial Intelligence, Information Systems and Molecular Biology. According to data from OpenAlex, Xiangchen Song has authored 13 papers receiving a total of 173 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 4 papers in Information Systems and 3 papers in Molecular Biology. Recurrent topics in Xiangchen Song's work include Topic Modeling (7 papers), EEG and Brain-Computer Interfaces (2 papers) and Biomedical Text Mining and Ontologies (2 papers). Xiangchen Song is often cited by papers focused on Topic Modeling (7 papers), EEG and Brain-Computer Interfaces (2 papers) and Biomedical Text Mining and Ontologies (2 papers). Xiangchen Song collaborates with scholars based in United States, China and Hong Kong. Xiangchen Song's co-authors include Yujing Wang, Yaming Yang, Jing Bai, Jiawei Han, Irwin King, Yankai Chen, Yan Zhang, Yuan Zhang, Jieyu Zhang and Xuan Wang and has published in prestigious journals such as PLoS ONE, IEEE Transactions on Wireless Communications and Europe PMC (PubMed Central).

In The Last Decade

Xiangchen Song

11 papers receiving 173 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiangchen Song United States 8 142 81 26 25 18 13 173
Deming Ye China 7 265 1.9× 53 0.7× 36 1.4× 28 1.1× 35 1.9× 7 301
Yusuke Shinyama United States 6 365 2.6× 83 1.0× 22 0.8× 27 1.1× 24 1.3× 8 395
Benfeng Xu China 6 230 1.6× 29 0.4× 34 1.3× 27 1.1× 28 1.6× 14 261
Stefania Racioppa Germany 5 118 0.8× 53 0.7× 15 0.6× 16 0.6× 11 0.6× 11 137
Myung-Gil Jang South Korea 9 268 1.9× 91 1.1× 35 1.3× 15 0.6× 6 0.3× 29 310
Sang-Hyeun Park Germany 6 138 1.0× 34 0.4× 30 1.2× 22 0.9× 20 1.1× 7 156
Gabriel Pereira Lopes Portugal 10 216 1.5× 37 0.5× 17 0.7× 16 0.6× 9 0.5× 42 245
Samuel Mensah Canada 9 469 3.3× 61 0.8× 16 0.6× 17 0.7× 13 0.7× 24 499

Countries citing papers authored by Xiangchen Song

Since Specialization
Citations

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

Fields of papers citing papers by Xiangchen Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiangchen Song

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

All Works

13 of 13 papers shown
1.
Song, Xiangchen, et al.. (2024). A randomized controlled trial on anonymizing reviewers to each other in peer review discussions. PLoS ONE. 19(12). e0315674–e0315674.
2.
Cao, Defu, et al.. (2023). Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders. Proceedings of the AAAI Conference on Artificial Intelligence. 37(6). 6897–6905. 7 indexed citations
3.
Yu, Yue, Xuan Kan, Hejie Cui, et al.. (2023). Deep Dag Learning of Effective Brain Connectivity for FMRI Analysis. PubMed. 2023. 1–5. 1 indexed citations
4.
Yang, Yaming, Xiangchen Song, Yuan Zhang, et al.. (2022). Learning Multi-granularity Consecutive User Intent Unit for Session-based Recommendation. arXiv (Cornell University). 343–352. 43 indexed citations
5.
Song, Xiangchen, et al.. (2022). TaxoEnrich: Self-Supervised Taxonomy Completion via Structure-Semantic Representations. Proceedings of the ACM Web Conference 2022. 925–934. 16 indexed citations
6.
Chen, Yankai, Yaming Yang, Yujing Wang, et al.. (2022). Attentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Personalized Recommendation. 2022 IEEE 38th International Conference on Data Engineering (ICDE). 299–311. 44 indexed citations
7.
Gao, Yayu, et al.. (2022). When Aloha and CSMA Coexist: Modeling, Fairness, and Throughput Optimization. IEEE Transactions on Wireless Communications. 21(10). 8163–8178. 4 indexed citations
8.
Yu, Yue, Xuan Kan, Hejie Cui, et al.. (2022). Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks (Extended Abstract). 2022 IEEE International Conference on Big Data (Big Data). 4995–4996. 5 indexed citations
9.
Wang, Xuan, et al.. (2021). ChemNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided Distant Supervision. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 5227–5240. 16 indexed citations
10.
Zhang, Jieyu, et al.. (2021). Taxonomy Completion via Triplet Matching Network. Proceedings of the AAAI Conference on Artificial Intelligence. 35(5). 4662–4670. 16 indexed citations
11.
12.
13.
Wang, Xuan, et al.. (2020). Comprehensive named entity recognition on CORD-19 with distant or weak supervision. Europe PMC (PubMed Central). 9.

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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2026