What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Database
His scientific interests lie mostly in Data mining, Web search query, Artificial intelligence, Information retrieval and World Wide Web.
Kevin Chen-Chuan Chang has researched Data mining in several fields, including Event, Complex event processing, Rank and Search engine indexing.
His studies deal with areas such as Data integration and Schema matching as well as Web search query.
His Artificial intelligence research includes elements of Machine learning, Set, Multimedia and Natural language processing.
As part of his studies on Information retrieval, Kevin Chen-Chuan Chang often connects relevant areas like Ranking.
His World Wide Web research focuses on Database and how it connects with Leverage.
His most cited work include:
- A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications (711 citations)
- Structured databases on the web: observations and implications (342 citations)
- TEDAS: A Twitter-based Event Detection and Analysis System (340 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Information retrieval, Theoretical computer science, Data mining, Web search query and Artificial intelligence.
His Information retrieval research incorporates elements of World Wide Web and Information integration.
The Theoretical computer science study combines topics in areas such as Graph, Closeness, Embedding, Graph and Node.
His work in Data mining addresses issues such as Probabilistic logic, which are connected to fields such as Data stream mining.
His work in Web search query covers topics such as Schema matching which are related to areas like Synonym and Star schema.
Kevin Chen-Chuan Chang has included themes like Machine learning, Pattern recognition and Natural language processing in his Artificial intelligence study.
He most often published in these fields:
- Information retrieval (25.77%)
- Theoretical computer science (25.15%)
- Data mining (19.63%)
What were the highlights of his more recent work (between 2018-2021)?
- Theoretical computer science (25.15%)
- Graph (13.50%)
- Artificial intelligence (17.79%)
In recent papers he was focusing on the following fields of study:
Theoretical computer science, Graph, Artificial intelligence, Embedding and Graph are his primary areas of study.
His Theoretical computer science research includes themes of Node, Classifier, Application programming interface and Oracle.
His work deals with themes such as Artificial neural network and Probabilistic logic, which intersect with Graph.
His studies in Artificial intelligence integrate themes in fields like Machine learning and Natural language processing.
His Graph embedding study in the realm of Embedding interacts with subjects such as Network geometry.
His research integrates issues of Social media, Microblogging and Information retrieval in his study of Graph.
Between 2018 and 2021, his most popular works were:
- Geom-GCN: Geometric Graph Convolutional Networks (55 citations)
- Embedding Both Finite and Infinite Communities on Graphs [Application Notes] (22 citations)
- Meta-GNN: metagraph neural network for semi-supervised learning in attributed heterogeneous information networks (17 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Database
Kevin Chen-Chuan Chang mainly focuses on Theoretical computer science, Embedding, Graph, Artificial neural network and Graph neural networks.
Kevin Chen-Chuan Chang performs multidisciplinary studies into Theoretical computer science and Motif in his work.
His study in Graph is interdisciplinary in nature, drawing from both Matching, Representation, Proximity search and Class.
The various areas that Kevin Chen-Chuan Chang examines in his Artificial neural network study include Semi-supervised learning, Feature extraction and Graph.
His research in Graph neural networks intersects with topics in Spatial network, Feature learning and GEOM.
He combines subjects such as Bipartite graph, Graph drawing, Inference and Graph embedding with his study of Node.
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