Congbo Ma

661 total citations
11 papers, 213 citations indexed

About

Congbo Ma is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Congbo Ma has authored 11 papers receiving a total of 213 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 1 paper in Computer Networks and Communications. Recurrent topics in Congbo Ma's work include Topic Modeling (3 papers), Advanced Text Analysis Techniques (3 papers) and Natural Language Processing Techniques (2 papers). Congbo Ma is often cited by papers focused on Topic Modeling (3 papers), Advanced Text Analysis Techniques (3 papers) and Natural Language Processing Techniques (2 papers). Congbo Ma collaborates with scholars based in Australia, United Kingdom and China. Congbo Ma's co-authors include Wei Emma Zhang, Quan Z. Sheng, Mingyu Guo, Yuanhong Chen, M. Louise Hull, Jodie Avery, Gustavo Carneiro, Qi Wu, Ruoxi Sun and Huaming Chen and has published in prestigious journals such as ACM Computing Surveys, Physics in Medicine and Biology and IEEE Transactions on Computational Social Systems.

In The Last Decade

Congbo Ma

9 papers receiving 206 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Congbo Ma Australia	6	105	67	25	18	15	11	213
Jindong Gu United Kingdom	7	149 1.4×	78 1.2×	11 0.4×	13 0.7×	20 1.3×	32	217
Saeed Ali Bahaj Saudi Arabia	10	100 1.0×	83 1.2×	22 0.9×	34 1.9×	20 1.3×	25	307
Xiangfeng Wang China	6	63 0.6×	60 0.9×	20 0.8×	26 1.4×	7 0.5×	20	171
Chandradeep Bhatt India	5	90 0.9×	50 0.7×	12 0.5×	23 1.3×	8 0.5×	63	260
Vinoth Kumar Venkatesan India	6	90 0.9×	29 0.4×	37 1.5×	32 1.8×	10 0.7×	9	238
Jun Hou China	7	57 0.5×	44 0.7×	23 0.9×	12 0.7×	7 0.5×	30	139
Myasar Mundher Adnan Iraq	9	71 0.7×	111 1.7×	15 0.6×	29 1.6×	26 1.7×	78	258
Shukang Yin China	4	97 0.9×	61 0.9×	14 0.6×	12 0.7×	7 0.5×	6	218
Qiushan Guo China	5	148 1.4×	182 2.7×	14 0.6×	14 0.8×	13 0.9×	9	282
Marcin Gabryel Poland	5	59 0.6×	41 0.6×	14 0.6×	18 1.0×	13 0.9×	13	148

Countries citing papers authored by Congbo Ma

Since Specialization

Citations

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

Fields of papers citing papers by Congbo Ma

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Congbo Ma

This figure shows the co-authorship network connecting the top 25 collaborators of Congbo Ma. A scholar is included among the top collaborators of Congbo Ma 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 Congbo Ma. Congbo Ma 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:

11 of 11 papers shown

1.

Li, Qing, et al.. (2025). HD-NDEs: Neural Differential Equations for Hallucination Detection in LLMs. Fraunhofer-Publica (Fraunhofer-Gesellschaft). 6173–6186.

2.

Butler, David, Jodie Avery, Steven Knox, et al.. (2024). Human–AI collaborative multi-modal multi-rater learning for endometriosis diagnosis. Physics in Medicine and Biology. 70(1). 15008–15008. 5 indexed citations

3.

Ma, Congbo, et al.. (2024). Disentangling Specificity for Abstractive Multi-document Summarization. 1–8. 1 indexed citations

4.

Ma, Congbo, et al.. (2024). An Efficient Automatic Meta-Path Selection for Social Event Detection via Hyperbolic Space. 2519–2529.

5.

Sun, Ruoxi, et al.. (2023). Data Hiding With Deep Learning: A Survey Unifying Digital Watermarking and Steganography. IEEE Transactions on Computational Social Systems. 10(6). 2985–2999. 37 indexed citations

6.

Chen, Yuanhong, et al.. (2023). Multi-Modal Learning with Missing Modality via Shared-Specific Feature Modelling. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 15878–15887. 65 indexed citations

7.

Ma, Congbo, et al.. (2022). Multi-document Summarization via Deep Learning Techniques: A Survey. ACM Computing Surveys. 55(5). 1–37. 58 indexed citations

8.

Chen, Hao, et al.. (2021). Multi-Intersection Traffic Optimisation: A Benchmark Dataset and a Strong Baseline. IEEE Open Journal of Intelligent Transportation Systems. 3. 126–136. 12 indexed citations

9.

Ma, Congbo. (2021). Improving Deep Learning based Multi-document Summarization through Linguistic Knowledge. 2704–2704. 1 indexed citations

10.

Zhang, Wei Emma, Quan Z. Sheng, Adnan Mahmood, et al.. (2020). The 10 Research Topics in the Internet of Things. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 34–43. 32 indexed citations

11.

Hu, Wang, et al.. (2017). A Fast Image Matting Method Based on Interval-Line Sampling. 912–915. 2 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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