Bo Zong

3.5k total citations · 3 hit papers
33 papers, 2.0k citations indexed

About

Bo Zong is a scholar working on Artificial Intelligence, Computer Networks and Communications and Signal Processing. According to data from OpenAlex, Bo Zong has authored 33 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Artificial Intelligence, 11 papers in Computer Networks and Communications and 10 papers in Signal Processing. Recurrent topics in Bo Zong's work include Advanced Graph Neural Networks (10 papers), Topic Modeling (6 papers) and Complex Network Analysis Techniques (6 papers). Bo Zong is often cited by papers focused on Advanced Graph Neural Networks (10 papers), Topic Modeling (6 papers) and Complex Network Analysis Techniques (6 papers). Bo Zong collaborates with scholars based in United States, China and Japan. Bo Zong's co-authors include Wei Cheng, Haifeng Chen, Cristian Lumezanu, Jingchao Ni, Song Qi, Dae-Ki Cho, Martin Renqiang Min, Dongjin Song, Yuncong Chen and Wenchao Yu and has published in prestigious journals such as IEEE Transactions on Knowledge and Data Engineering, Proceedings of the VLDB Endowment and Journal of Physics Conference Series.

In The Last Decade

Bo Zong

33 papers receiving 1.9k citations

Hit Papers

Deep Autoencoding Gaussian Mixture Model for Unsupervised... 2018 2026 2020 2023 2018 2019 2021 200 400 600
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.

  1. Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection
    2018 628 citations Bo Zong, Wei Cheng et al. profile →
  2. A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data
    2019 482 citations Chuxu Zhang, Dongjin Song et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →
  3. Learning to Drop: Robust Graph Neural Network via Topological Denoising
    2021 156 citations Dongsheng Luo, Wei Cheng et al. profile →

Peers

Bo Zong
Wei Cheng United States
Xiaofei Xu China
Emmanuel Müller Germany
Haifeng Chen United States
Jingchao Ni United States
Ira Assent Denmark
Cristian Lumezanu United States
Pietro Michiardi France
Hiroyuki Kitagawa Japan
Sarah Erfani Australia
Wei Cheng United States
Bo Zong
Citations per year, relative to Bo Zong Bo Zong (= 1×) peers Wei Cheng

Countries citing papers authored by Bo Zong

Since Specialization
Citations

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

Fields of papers citing papers by Bo Zong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Bo Zong

This figure shows the co-authorship network connecting the top 25 collaborators of Bo Zong. A scholar is included among the top collaborators of Bo Zong 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 Zong. Bo Zong 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
1.
Song, Dongjin, Yuncong Chen, Wei Cheng, et al.. (2022). Deep Federated Anomaly Detection for Multivariate Time Series Data. 2022 IEEE International Conference on Big Data (Big Data). 1–10. 3 indexed citations
2.
Luo, Dongsheng, Wei Cheng, Wenchao Yu, et al.. (2021). Learning to Drop: Robust Graph Neural Network via Topological Denoising. 779–787. 156 indexed citations breakdown →
3.
Zhang, Xuchao, Bo Zong, Wei Cheng, et al.. (2021). Unsupervised Concept Representation Learning for Length-Varying Text Similarity. 5611–5620. 1 indexed citations
4.
Ni, Jingchao, Wei Cheng, Bo Zong, et al.. (2021). Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series. Proceedings of the AAAI Conference on Artificial Intelligence. 35(1). 651–659. 35 indexed citations
5.
Xu, Dongkuan, Wei Cheng, Xin Dong, et al.. (2021). Multi-Task Recurrent Modular Networks. Proceedings of the AAAI Conference on Artificial Intelligence. 35(12). 10496–10504. 1 indexed citations
6.
Wang, Zhen, Bo Zong, & Huan Sun. (2021). Modeling Context Pair Interaction for Pairwise Tasks on Graphs. 851–859. 1 indexed citations
7.
Wang, Lichen, Bo Zong, Wei Cheng, et al.. (2020). Inductive and Unsupervised Representation Learning on Graph Structured Objects. International Conference on Learning Representations. 6 indexed citations
8.
Song, Dongjin, Yuncong Chen, Cristian Lumezanu, et al.. (2020). Deep Unsupervised Binary Coding Networks for Multivariate Time Series Retrieval. Proceedings of the AAAI Conference on Artificial Intelligence. 34(2). 1403–1411. 8 indexed citations
9.
Xu, Dongkuan, Wei Cheng, Bo Zong, et al.. (2020). Tensorized LSTM with Adaptive Shared Memory for Learning Trends in Multivariate Time Series. Proceedings of the AAAI Conference on Artificial Intelligence. 34(2). 1395–1402. 31 indexed citations
10.
Zhang, Chuxu, Dongjin Song, Yuncong Chen, et al.. (2019). A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 1409–1416. 482 indexed citations breakdown →
11.
Zhao, Xiaohan, Bo Zong, Ziyu Guan, Kai Zhang, & Wei Zhao. (2018). Substructure Assembling Network for Graph Classification. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 12 indexed citations
12.
Yu, Wenchao, Cheng Zheng, Wei Cheng, et al.. (2018). Learning Deep Network Representations with Adversarially Regularized Autoencoders. 2663–2671. 131 indexed citations
13.
Song, Qi, Bo Zong, Yinghui Wu, et al.. (2018). TGNet. 97–106. 9 indexed citations
14.
Zong, Bo, et al.. (2016). The Bayesian Reliability Assessment and Prediction for Radar System Based on New Dirichlet Prior Distribution. Journal of Physics Conference Series. 679. 12039–12039. 3 indexed citations
15.
Zong, Bo, Xusheng Xiao, Zhichun Li, et al.. (2015). Behavior query discovery in system-generated temporal graphs. Proceedings of the VLDB Endowment. 9(4). 240–251. 16 indexed citations
16.
Singh, Vishwakarma, Bo Zong, & Ambuj K. Singh. (2015). Nearest Keyword Set Search in Multi-Dimensional Datasets. IEEE Transactions on Knowledge and Data Engineering. 28(3). 741–755. 6 indexed citations
17.
Zong, Bo, Christos Gkantsidis, & Milan Vojnović. (2015). Herding "small" streaming queries. London School of Economics and Political Science Research Online (London School of Economics and Political Science). 46–57. 1 indexed citations
18.
Mongiovı̀, Misael, Ambuj K. Singh, Xifeng Yan, Bo Zong, & Konstantinos Psounis. (2012). Efficient multicasting for delay tolerant networks using graph indexing. 1386–1394. 39 indexed citations
19.
Yang, Shengqi, Xifeng Yan, Bo Zong, & Arijit Khan. (2012). Towards effective partition management for large graphs. 517–528. 103 indexed citations
20.
Zong, Bo, Yinghui Wu, Ambuj K. Singh, & Xifeng Yan. (2012). Inferring the Underlying Structure of Information Cascades. 1218–1223. 15 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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