Guojie Song
- Transportation top 0.5%
- Human Mobility and Location-Based Analysis 12
- Transportation Planning and Optimization 10
- Statistical and Nonlinear Physics top 0.5%
- Complex Network Analysis Techniques 32
- Opinion Dynamics and Social Influence 10
- Building and Construction top 0.5%
- Traffic Prediction and Management Techniques 18
- Artificial Intelligence top 1%
- Advanced Graph Neural Networks 35
- Information Systems top 1%
-
- Data Management and Algorithms 12
-
- Graph Theory and Algorithms 9
- Co-authors
- Kunqing XieHaikun HongWenhao HuangGao CongJunshan WangYu WangXinran HeWei Chen
- Journals
- Scientific Reports (1 paper)IEEE Transactions on Intelligent Transportation Systems (1 paper)Neural Computation (1 paper)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Guojie Song
103 papers receiving 3.6k citations
Hit Papers
Peers
Comparison fields: 5 of 111
- Transportation 810
- Statistical and Nonlinear Physics 1.4k
- Building and Construction 861
- Artificial Intelligence 1.2k
- Information Systems 576
Countries citing papers authored by Guojie Song
This map shows the geographic impact of Guojie Song'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 Guojie Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guojie Song more than expected).
Fields of papers citing papers by Guojie Song
This network shows the impact of papers produced by Guojie Song. 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 Guojie Song. The network helps show where Guojie Song may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Guojie Song, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2023 | 24 | |
| 4 | 2023 | 2 | |
| 5 | 2023 | 1 | |
| 6 | 2020 | 2 | |
| 7 | 2020 | 79 | |
| 8 | 2018 | 6 | |
| 9 | 2017 | 53 | |
| 10 | 2015 | 1 | |
| 11 | 2015 | 9 | |
| 12 | 2015 | 3 | |
| 13 | 2014 | 4 | |
| 14 | Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learningbreakdown → | 2014 | 901 |
| 15 | 2012 | 271 | |
| 16 | 2012 | 2 | |
| 17 | 2008 | 18 | |
| 18 | 2007 | 11 | |
| 19 | 2006 | 18 | |
| 20 | A Spatial Feature Selection Method Based on Maximum Entropy Theory | 2003 | 5 |
About Guojie Song
Guojie Song is a scholar working on Transportation, Statistical and Nonlinear Physics and Artificial Intelligence, having authored 106 papers that have together received 3.7k indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (35 papers), Complex Network Analysis Techniques (32 papers), Traffic Prediction and Management Techniques (18 papers), Data Management and Algorithms (12 papers), Human Mobility and Location-Based Analysis (12 papers), Transportation Planning and Optimization (10 papers), Opinion Dynamics and Social Influence (10 papers) and Graph Theory and Algorithms (9 papers). The work is most often cited by research in Transportation (810 citations), Statistical and Nonlinear Physics (1.4k citations) and Building and Construction (861 citations). Guojie Song has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Kunqing Xie, Haikun Hong, Wenhao Huang, Gao Cong, Junshan Wang, Yu Wang, Xinran He, Wei Chen, Chuan Shi and Xiao Wang. Their work appears in journals such as Scientific Reports, IEEE Transactions on Intelligent Transportation Systems and Neural Computation.
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.