What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Data mining
Xifeng Yan mainly investigates Data mining, Graph, Artificial intelligence, Machine learning and Theoretical computer science.
The concepts of his Data mining study are interwoven with issues in Graph, Set, Support vector machine and Molecule mining.
Xifeng Yan has included themes like Data mining algorithm, Scalability, Discriminative model and Substructure in his Graph study.
His Substructure research is multidisciplinary, relying on both Frequent subtree mining, Graph based, Computation and Lexicographical order.
His Theoretical computer science research is multidisciplinary, incorporating elements of Graph property, SPARQL, Graph operations and Random graph.
Xifeng Yan combines subjects such as Sequential Pattern Mining and Data science with his study of Cluster analysis.
His most cited work include:
- gSpan: graph-based substructure pattern mining (1831 citations)
- Frequent pattern mining: current status and future directions (1133 citations)
- PathSim: meta path-based top-K similarity search in heterogeneous information networks (964 citations)
What are the main themes of his work throughout his whole career to date?
Xifeng Yan focuses on Data mining, Artificial intelligence, Graph, Theoretical computer science and Graph.
His work focuses on many connections between Data mining and other disciplines, such as Discriminative model, that overlap with his field of interest in Feature selection.
His Artificial intelligence study incorporates themes from Natural language processing, Machine learning and Pattern recognition.
His biological study spans a wide range of topics, including Scalability, Nearest neighbor search and Information retrieval.
His Theoretical computer science research integrates issues from Modular decomposition, Subgraph isomorphism problem, Probabilistic logic, Semantics and Partition.
His work in Graph is not limited to one particular discipline; it also encompasses Molecule mining.
He most often published in these fields:
- Data mining (38.99%)
- Artificial intelligence (26.15%)
- Graph (24.31%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (26.15%)
- Machine learning (12.84%)
- Natural language processing (5.50%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Natural language processing, Relation and Embedding.
His work on Natural language, Knowledge base and Generalization error as part of general Artificial intelligence study is frequently connected to Conversation, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Machine learning study combines topics from a wide range of disciplines, such as Text corpus, Graph neural networks and Text generation.
His Graph neural networks study necessitates a more in-depth grasp of Graph.
Xifeng Yan interconnects Upper and lower bounds and Graph in the investigation of issues within Graph.
His Embedding study integrates concerns from other disciplines, such as Concept mining and Information retrieval.
Between 2016 and 2021, his most popular works were:
- Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting (71 citations)
- Cross-domain Semantic Parsing via Paraphrasing (54 citations)
- Variational Knowledge Graph Reasoning (53 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Programming language
Xifeng Yan spends much of his time researching Artificial intelligence, Dialog box, Theoretical computer science, Natural language and Semantics.
His studies in Artificial intelligence integrate themes in fields like Relation and Natural language processing.
His work deals with themes such as Machine learning, Scope and Sequence, which intersect with Relation.
The study incorporates disciplines such as Semantic reasoner, Latent variable, Inference, Upper and lower bounds and Existential quantification in addition to Theoretical computer science.
In his research on the topic of Natural language, SIMPLE, Deep learning, Information retrieval, Domain and Paraphrase is strongly related with Parsing.
His Semantics study combines topics in areas such as Response generation, Graph, Self attention and Dialog act.
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