What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His scientific interests lie mostly in Data mining, Artificial intelligence, Embedding, Machine learning and Data science.
His research on Data mining focuses in particular on Association rule learning.
His Artificial intelligence research is multidisciplinary, incorporating elements of Graph and Pattern recognition.
His Embedding research integrates issues from Theoretical computer science, Artificial neural network, Representation, Semantics and Design structure matrix.
His Machine learning research includes themes of Contextual image classification, Network topology and FSA-Red Algorithm.
His research in Data science intersects with topics in Domain, Enterprise data management, Decision support system, Knowledge extraction and Concept mining.
His most cited work include:
- A Comprehensive Survey on Graph Neural Networks (1287 citations)
- Efficient mining of both positive and negative association rules (389 citations)
- Association Rule Mining: Models and Algorithms (301 citations)
What are the main themes of his work throughout his whole career to date?
Chengqi Zhang mainly investigates Artificial intelligence, Data mining, Machine learning, Data science and Theoretical computer science.
His Artificial intelligence study typically links adjacent topics like Pattern recognition.
His Data mining study focuses on Association rule learning in particular.
Chengqi Zhang frequently studies issues relating to Benchmark and Machine learning.
His research integrates issues of Domain and Knowledge extraction in his study of Data science.
His studies deal with areas such as Node, Embedding and Graph as well as Theoretical computer science.
He most often published in these fields:
- Artificial intelligence (34.90%)
- Data mining (29.02%)
- Machine learning (18.04%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (34.90%)
- Machine learning (18.04%)
- Theoretical computer science (9.41%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Machine learning, Theoretical computer science, Graph and Data mining are his primary areas of study.
Artificial intelligence is closely attributed to Pattern recognition in his work.
His work carried out in the field of Machine learning brings together such families of science as Field, Training set and Social network.
His Theoretical computer science research integrates issues from Embedding, Clustering coefficient, Cluster analysis, Node and Nearest neighbor search.
His study in Graph is interdisciplinary in nature, drawing from both Feature extraction, Graph and Kernel.
His Data mining study integrates concerns from other disciplines, such as Computational biology and Series.
Between 2015 and 2021, his most popular works were:
- A Comprehensive Survey on Graph Neural Networks (1287 citations)
- DiSAN: Directional Self-Attention Network for RNN/CNN-free Language Understanding (299 citations)
- Tri-party deep network representation (232 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Chengqi Zhang focuses on Artificial intelligence, Theoretical computer science, Machine learning, Embedding and Graph.
His biological study spans a wide range of topics, including Algorithm design and Pattern recognition.
His Machine learning research is multidisciplinary, incorporating perspectives in Field, Network topology and Big data.
His Embedding study combines topics in areas such as Artificial neural network, Representation and Clustering coefficient, Cluster analysis.
He has researched Graph in several fields, including Feature extraction and Graph.
His Feature study deals with Feature selection intersecting with Data mining.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
