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.
Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning
2021276 citationsYu Tian, Guansong Pang et al.profile →
Deep Anomaly Detection with Deviation Networks
2019250 citationsGuansong Pang, Chunhua Shen et al.profile →
Viral Pneumonia Screening on Chest X-Rays Using Confidence-Aware Anomaly Detection
2020247 citationsGuansong Pang, Chunhua Shen et al.profile →
Deep Isolation Forest for Anomaly Detection
2023189 citationsHongzuo Xu, Guansong Pang et al.IEEE Transactions on Knowledge and Data Engineeringprofile →
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
202483 citationsKexin Zhang, Qingsong Wen et al.IEEE Transactions on Pattern Analysis and Machine Intelligenceprofile →
VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection
202454 citationsPeng Wu, Xuerong Zhou et al.Proceedings of the AAAI Conference on Artificial Intelligenceprofile →
Calibrated One-Class Classification for Unsupervised Time Series Anomaly Detection
202448 citationsHongzuo Xu, Yijie Wang et al.IEEE Transactions on Knowledge and Data Engineeringprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Guansong Pang'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 Guansong Pang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guansong Pang more than expected).
This network shows the impact of papers produced by Guansong Pang. 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 Guansong Pang. The network helps show where Guansong Pang may publish in the future.
Co-authorship network of co-authors of Guansong Pang
This figure shows the co-authorship network connecting the top 25 collaborators of Guansong Pang.
A scholar is included among the top collaborators of Guansong Pang 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 Guansong Pang. Guansong Pang is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zhang, Kexin, Qingsong Wen, Chaoli Zhang, et al.. (2024). Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(10). 6775–6794.83 indexed citations breakdown →
Xu, Hongzuo, et al.. (2024). Calibrated One-Class Classification for Unsupervised Time Series Anomaly Detection. IEEE Transactions on Knowledge and Data Engineering. 36(11). 5723–5736.48 indexed citations breakdown →
Xu, Hongzuo, Guansong Pang, Yijie Wang, & Yongjun Wang. (2023). Deep Isolation Forest for Anomaly Detection. IEEE Transactions on Knowledge and Data Engineering. 35(12). 12591–12604.189 indexed citations breakdown →
14.
Pang, Guansong, Charų C. Aggarwal, Chunhua Shen, & Nicu Sebe. (2022). Editorial Deep Learning for Anomaly Detection. IEEE Transactions on Neural Networks and Learning Systems. 33(6). 2282–2286.8 indexed citations
Yan, Cheng, Guansong Pang, Xiao Bai, et al.. (2019). Deep Hashing by Discriminating Hard Examples. Griffith Research Online (Griffith University, Queensland, Australia). 1535–1542.23 indexed citations
Pang, Guansong, Longbing Cao, & Ling Chen. (2016). Outlier detection in complex categorical data by modelling the feature value couplings. UTS ePRESS (University of Technology Sydney). 1902–1908.40 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.