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.
Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach
2018317 citationsLei Lin et al.Transportation Research Part C Emerging Technologiesprofile →
Vehicle Trajectory Prediction Using LSTMs With Spatial–Temporal Attention Mechanisms
2021181 citationsLei Lin, Weizi Li et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Lei Lin'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 Lei Lin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lei Lin more than expected).
This network shows the impact of papers produced by Lei Lin. 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 Lei Lin. The network helps show where Lei Lin may publish in the future.
Co-authorship network of co-authors of Lei Lin
This figure shows the co-authorship network connecting the top 25 collaborators of Lei Lin.
A scholar is included among the top collaborators of Lei Lin 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 Lei Lin. Lei Lin is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Lin, Lei, et al.. (2019). Medical Time Series Classification with Hierarchical Attention-based Temporal Convolutional Networks: A Case Study of Myotonic Dystrophy Diagnosis. Computer Vision and Pattern Recognition. 83–86.1 indexed citations
6.
Lin, Lei, et al.. (2019). AutoMPC: Efficient Multi-Party Computation for Secure and Privacy-Preserving Cooperative Control of Connected Autonomous Vehicles.. National Conference on Artificial Intelligence.5 indexed citations
Sun, Chengjie, et al.. (2016). Enlarging drug dictionary with semi-supervised learning for Drug Entity Recognition. IEEE Conference Proceedings. 2016. 1931.1 indexed citations
Lin, Lei, et al.. (2015). An Android Smartphone Application for Collecting, Sharing, and Predicting Border Crossing Wait Time. Transportation Research Board 94th Annual MeetingTransportation Research Board.2 indexed citations
11.
Liu, Yang, Chengjie Sun, Lei Lin, Xiaolong Wang, & Yuming Zhao. (2015). Computing Semantic Text Similarity Using Rich Features. Waseda University Repository (Waseda University). 44–52.9 indexed citations
12.
Yamamoto, Yusuke, et al.. (2013). A Development and Evaluation of Variable Speed Charger for Lithium-ion Battery Aiming at Cool Charging. IEICE Technical Report; IEICE Tech. Rep.. 113(118). 147–152.1 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.