Songjie Wang

1.5k citations
57 papers · 1.1k indexed · 1 hit paper · h-index 18
Topics
IoT and Edge/Fog Computing (6 papers)Scientific Computing and Data Management (5 papers)Advanced Neural Network Applications (5 papers)
Partner nations
United StatesChinaIndia

In The Last Decade

Songjie Wang

56 papers receiving 1.1k citations

Hit Papers

Evaluation of the Criteria to Distinguish Left Bundle Bra...202120262022202420214080120

Peers

Songjie Wang
Comparison fields: 5 of 136
  • Cognitive Neuroscience 179
  • Information Systems 172
  • Computer Networks and Communications 163
  • Cardiology and Cardiovascular Medicine 161
  • Artificial Intelligence 132
Replace Charles P. Unsworth with:
Charles P. Unsworth New Zealand
Jianzhong Wang China
Shouyi Wang United States
Zhidong Zhao China
Arpit Bhardwaj India
Yanjiang Wang China
Zhiyuan Luo United Kingdom
Wei Du China
Brian C. Ross United States
R. Rajesh India
Songjie Wang relative to Charles P. Unsworth New Zealand Charles P. Unsworth's profile →
Citations per field
00.5×
Charles P. Unsworth · 1×
Citations per year

Countries citing papers authored by Songjie Wang

Since Specialization
Citations

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

Fields of papers citing papers by Songjie Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Songjie Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Songjie Wang. A scholar is included among the top collaborators of Songjie Wang 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 Songjie Wang. Songjie Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 1
2 0
3 1
4 1
5 10
6 6
7 10
8 27
9 14
10 5
11 7
12 16
13 2
14 20
15 10
16 20
17
Influence of processing parameters and their interaction on shrinkage of injection molded parts
1
18 37
19 21
20
Parameter Simulation and Composition 0f Viscosity Model in Injection Mould CAE
1

About Songjie Wang

Songjie Wang is a scholar working on Information Systems and Management, Computer Networks and Communications and Information Systems, having authored 57 papers that have together received 1.1k indexed citations. Recurring topics across this work include IoT and Edge/Fog Computing (6 papers), Scientific Computing and Data Management (5 papers) and Advanced Neural Network Applications (5 papers). The work is most often cited by research in Cognitive Neuroscience (179 citations), Information Systems (172 citations) and Computer Networks and Communications (163 citations). Songjie Wang has collaborated with scholars based in United States, China and India. Frequent co-authors include Prasad Calyam, Tzung‐Pei Hong, Gao‐Jun Teng, Muzhi Yang, Z ZHANG, Zhen Ma, Hao Zhang, Brenda T. Beerntsen, Juan Xing and Amod A. Ogale. Their work appears in journals such as Circulation, Scientific Reports and Expert Systems with Applications.

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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