Shu Wu

8.6k citations
106 papers · 3.9k · 5 hit papers · h-index 28

Impact in

Papers in

Shu Wu

101 papers receiving 3.8k citations

Shu Wu's Hit Papers

Dynamic Graph Neural Networks for Sequential Recommendation 2022 · 145 citations
1450+3+6Years since publication200400600

Peers

Shu Wu
Comparison fields: 5 of 138
  • Information Systems 2.0k
  • Transportation 579
  • Artificial Intelligence 2.1k
  • Computational Mathematics 36
  • Computer Vision and Pattern Recognition 1.1k
Replace Chao Huang with:
Chao Huang China
Defu Lian China
Nicholas Jing Yuan China
Yong Ge United States
Fuzhen Zhuang China
Xueming Qian China
Yanjie Fu United States
Dingqi Yang China
Vincent W. Zheng Singapore
Yu Zheng China
Shu Wu relative to Chao Huang China Chao Huang's profile →
Citations per field
00.5×20×40×60×89.5×
Chao Huang · 1×
Citations per year

Countries citing papers authored by Shu Wu

Since Specialization
Citations

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

Fields of papers citing papers by Shu Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Shu Wu, 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 Shu Wu Line = papers co-authored together Shu Wu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 106 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts
Hit paper breakdown →
2016640
2
A Dynamic Recurrent Model for Next Basket Recommendation
Hit paper breakdown →
2016332
3
A Convolutional Approach for Misinformation Identification
Hit paper breakdown →
2017327
4 2020197
5
Mining Latent Structures for Multimedia Recommendation
Hit paper breakdown →
2021161
6
Dynamic Graph Neural Networks for Sequential Recommendation
Hit paper breakdown →
2022145
7 2018127
8 2016115
9 2011112
10 2015107
11 201794
12 201591
13 201774
14 202270
15 201762
16 202262
17 201959
18 201559
19 202054
20 202052

About Shu Wu

Shu Wu is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Sociology and Political Science and Statistical and Nonlinear Physics, having authored 106 papers that have together received 3.9k indexed citations. Recurring topics across this work include Recommender Systems and Techniques (33 papers), Advanced Graph Neural Networks (30 papers), Topic Modeling (28 papers), Misinformation and Its Impacts (14 papers), Spam and Phishing Detection (10 papers), Natural Language Processing Techniques (8 papers), Complex Network Analysis Techniques (8 papers) and Image Retrieval and Classification Techniques (8 papers). The work is most often cited by research in Information Systems (2.0k citations), Transportation (579 citations), Artificial Intelligence (2.1k citations), Computational Mathematics (36 citations) and Computer Vision and Pattern Recognition (1.1k citations). Shu Wu has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Liang Wang, Tieniu Tan, Qiang Liu, Qiang Liu, Qiyue Yin, Yu Feng, Feng Yu, Qiang Liu, Qiang Liu and Xueli Yu. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, Neurocomputing, Pattern Recognition, ACM Transactions on Intelligent Systems and Technology and IEEE Transactions on Neural Networks and Learning Systems.

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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