Silu He

411 citations
14 papers · 250 · 1 hit paper · h-index 7

Impact in

Papers in

Silu He

14 papers receiving 246 citations

Silu He's Hit Papers

LSTTN: A Long-Short Term Transformer-based spatiotemporal neural network for traffic flow forecasting 2024 · 81 citations
810+1Years since publication255075

Peers

Silu He
Comparison fields: 5 of 44
  • Transportation 53
  • Building and Construction 99
  • Signal Processing 35
  • Media Technology 23
  • Artificial Intelligence 65
Replace Qianxiong Xu with:
Qianxiong Xu Singapore
Jindong Han China
Fanglan Chen United States
Collin Meese United States
Hanhan Deng China
Gang Chuai China
Attila Matyas Nagy Hungary
Ziqian Lin China
Silu He relative to Qianxiong Xu Singapore Qianxiong Xu's profile →
Citations per field
00.5×2×3×3.8×
Qianxiong Xu · 1×
Citations per year

Countries citing papers authored by Silu He

Since Specialization
Citations

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

Fields of papers citing papers by Silu He

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Silu He, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Silu He Line = papers co-authored together Silu He links everyone, so they are left out of the graph.

All Works

14 of 14 papers shown
#Work
1
LSTTN: A Long-Short Term Transformer-based spatiotemporal neural network for traffic flow forecasting
Hit paper breakdown →
202481
2 202356
3 202348
4 202411
5 202310
6 20258
7 20257
8 20217
9 20255
10 20234
11 20224
12 20254
13 20253
14 20142

About Silu He

Silu He is a scholar working on Transportation, Building and Construction, Media Technology, Signal Processing and Control and Systems Engineering, having authored 14 papers that have together received 250 indexed citations. Recurring topics across this work include Traffic Prediction and Management Techniques (5 papers), Transportation Planning and Optimization (3 papers), Traffic control and management (3 papers), Human Mobility and Location-Based Analysis (3 papers), Domain Adaptation and Few-Shot Learning (2 papers), Advanced Graph Neural Networks (2 papers), Data Management and Algorithms (2 papers) and Remote-Sensing Image Classification (2 papers). The work is most often cited by research in Transportation (53 citations), Building and Construction (99 citations), Signal Processing (35 citations), Media Technology (23 citations) and Artificial Intelligence (65 citations). Silu He has collaborated with scholars based in China and United States. Frequent co-authors include Haifeng Li, Qinyao Luo, Yuhan Wang, Xing Han, Ling Zhao, Ronghua Du, Jiawei Zhu, Xuying Wang, Han Fu and Guangjun He. Their work appears in journals such as Symmetry, Knowledge-Based Systems, ChemCatChem, Information Sciences and Industrial & Engineering Chemistry Research.

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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