Bo Long

1.9k total citations · 1 hit paper
32 papers, 1.2k citations indexed

About

Bo Long is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Information Systems. According to data from OpenAlex, Bo Long has authored 32 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Artificial Intelligence, 8 papers in Statistical and Nonlinear Physics and 7 papers in Information Systems. Recurrent topics in Bo Long's work include Complex Network Analysis Techniques (8 papers), Advanced Clustering Algorithms Research (5 papers) and Topic Modeling (4 papers). Bo Long is often cited by papers focused on Complex Network Analysis Techniques (8 papers), Advanced Clustering Algorithms Research (5 papers) and Topic Modeling (4 papers). Bo Long collaborates with scholars based in United States, China and Singapore. Bo Long's co-authors include Philip S. Yu, Zhongfei Zhang, Shuang-Hong Yang, Hongyuan Zha, Zhaohui Zheng, Wu Xiaoyun, Alex Smola, Yi Chang, Lingfei Wu and Xiaojie Guo and has published in prestigious journals such as IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Circuits and Systems for Video Technology and International Journal of Electrical Power & Energy Systems.

In The Last Decade

Bo Long

27 papers receiving 1.1k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Graph Neural Networks for Natural Language Processing: A Survey

2023 134 citations Lingfei Wu, Yu Chen et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Bo Long United States	14	756	353	309	270	143	32	1.2k
Ziniu Hu United States	8	892 1.2×	232 0.7×	312 1.0×	217 0.8×	105 0.7×	12	1.2k
Yanfang Ye United States	15	942 1.2×	274 0.8×	473 1.5×	211 0.8×	157 1.1×	66	1.3k
Sanjay Kumar India	21	600 0.8×	423 1.2×	191 0.6×	183 0.7×	230 1.6×	100	1.3k
Bhaskar Biswas India	21	558 0.7×	597 1.7×	221 0.7×	117 0.4×	131 0.9×	86	1.1k
Hwanjo Yu South Korea	16	731 1.0×	172 0.5×	158 0.5×	169 0.6×	150 1.0×	42	1.1k
Xiaomei Yu China	16	600 0.8×	142 0.4×	378 1.2×	259 1.0×	199 1.4×	66	1.1k
Francesco Gullo Italy	21	593 0.8×	488 1.4×	196 0.6×	291 1.1×	266 1.9×	64	1.1k
Yingxia Shao China	22	1.1k 1.4×	179 0.5×	418 1.4×	574 2.1×	287 2.0×	84	1.5k
Justin Zhan United States	19	669 0.9×	125 0.4×	317 1.0×	179 0.7×	167 1.2×	69	1.1k
Bahman Bahmani United States	8	523 0.7×	253 0.7×	263 0.9×	340 1.3×	271 1.9×	11	956

Countries citing papers authored by Bo Long

Since Specialization

Citations

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

Fields of papers citing papers by Bo Long

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Bo Long

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Huang, Kun, et al.. (2024). An Improved Analytical Model for the Novel Counter-Rotating Axial-Flux Hybrid-Excitation Permanent Magnet Machine. 1–2.

2.

Huang, Kun, et al.. (2024). An Improved Analytical Model for the Novel Counter-Rotating Axial-flux Hybrid-excitation Permanent Magnet Machine. 1–5.

3.

Long, Bo, Yaojing Feng, Chenxi Xia, & Shoudao Huang. (2023). Comparison and Analysis of Unbalanced Magnetic Torque and Electromagnetic Characteristics of Axial-flux Permanent Magnet Machines. 1549–1554.

4.

Zhang, Yiming, et al.. (2023). Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation. 1(4). 1–22. 13 indexed citations

5.

Wu, Lingfei, Yu Chen, Kai Shen, et al.. (2023). Graph Neural Networks for Natural Language Processing: A Survey. 16(2). 119–328. 134 indexed citations breakdown →

6.

Zou, Yanyan, Xueying Zhang, Jing Zhou, et al.. (2023). Automatic product copywriting for e‐commerce. AI Magazine. 44(1). 41–53. 1 indexed citations

7.

Li, Hongxu, Bo Long, Tao Wang, Feng Zhou, & Zhengping Zhang. (2023). Ultrawide-color-gamut single-pixel dynamic color manipulation based on yarn muscles–graphene MEMS. Optics Communications. 554. 130192–130192.

8.

Zhang, Xueying, Yanyan Zou, Jing Zhou, et al.. (2022). Automatic Product Copywriting for E-commerce. Proceedings of the AAAI Conference on Artificial Intelligence. 36(11). 12423–12431. 10 indexed citations

9.

Guo, Xiaojie, Qingkai Zeng, Meng Jiang, et al.. (2022). Automatic Controllable Product Copywriting for E-Commerce. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2946–2956. 5 indexed citations

10.

Zhang, Xueying, Yunjiang Jiang, Yue Shang, et al.. (2021). DSGPT: Domain-Specific Generative Pre-Training of Transformers for Text Generation in E-commerce Title and Review Summarization. arXiv (Cornell University). 2146–2150. 9 indexed citations

11.

Yang, Yang, et al.. (2020). Deep Learning for Anomaly Detection. 3569–3570. 10 indexed citations

12.

Wang, Tie, et al.. (2020). Deep Learning for Anomaly Detection. 894–896. 58 indexed citations

13.

Long, Bo, Jiang Bian, Olivier Chapelle, et al.. (2014). Active Learning for Ranking through Expected Loss Optimization. IEEE Transactions on Knowledge and Data Engineering. 27(5). 1180–1191. 32 indexed citations

14.

Yang, Shuang-Hong, et al.. (2011). Like like alike. 537–546. 256 indexed citations

15.

Long, Bo, Olivier Chapelle, Ya Zhang, et al.. (2010). Active learning for ranking through expected loss optimization. 267–274. 54 indexed citations

16.

Long, Bo, Zhongfei Zhang, & Philip S. Yu. (2009). A general framework for relation graph clustering. Knowledge and Information Systems. 24(3). 393–413. 14 indexed citations

17.

Long, Bo, Philip S. Yu, & Zhongfei Zhang. (2008). A General Model for Multiple View Unsupervised Learning. 822–833. 176 indexed citations

18.

Long, Bo, et al.. (2007). Graph partitioning based on link distributions. National Conference on Artificial Intelligence. 578–583. 1 indexed citations

19.

Long, Bo, Zhongfei Zhang, Wu Xiaoyun, & Philip S. Yu. (2006). Spectral clustering for multi-type relational data. 585–592. 165 indexed citations

20.

Long, Bo, et al.. (1966). Fatigue Crack Propagation in Mild Steel. Canadian Metallurgical Quarterly. 5(1). 19–34. 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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