What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Machine learning
Ying Zhang mostly deals with Data mining, Theoretical computer science, Object, Search engine indexing and Graph.
His work carried out in the field of Data mining brings together such families of science as Data modeling, Spatial query, Set and k-nearest neighbors algorithm.
His Theoretical computer science study which covers Computation that intersects with Combinatorics, Core and Similarity.
His Search engine indexing research includes themes of Nearest neighbor search and Database.
His study on Subgraph isomorphism problem is often connected to Modular decomposition, Quality and Point as part of broader study in Graph.
His Skyline study integrates concerns from other disciplines, such as Time complexity and Dynamic programming, Mathematical optimization, Approximation algorithm.
His most cited work include:
- Selecting Stars: The k Most Representative Skyline Operator (342 citations)
- Taming verification hardness: an efficient algorithm for testing subgraph isomorphism (246 citations)
- Probabilistic Skyline Operator over Sliding Windows (111 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Theoretical computer science, Data mining, Graph, Set and Vertex.
He works mostly in the field of Theoretical computer science, limiting it down to concerns involving Pruning and, occasionally, Location-based service.
Ying Zhang combines subjects such as Object, Spatial query, Search engine indexing and k-nearest neighbors algorithm with his study of Data mining.
His Graph research is multidisciplinary, incorporating elements of Graph and Computation.
The various areas that Ying Zhang examines in his Set study include Ranking, Synthetic data, Containment and Skyline.
His work focuses on many connections between Vertex and other disciplines, such as Time complexity, that overlap with his field of interest in Mathematical optimization.
He most often published in these fields:
- Theoretical computer science (28.72%)
- Data mining (28.21%)
- Graph (21.54%)
What were the highlights of his more recent work (between 2019-2021)?
- Theoretical computer science (28.72%)
- Vertex (16.41%)
- Graph (21.54%)
In recent papers he was focusing on the following fields of study:
Ying Zhang mainly investigates Theoretical computer science, Vertex, Graph, Bipartite graph and Algorithm.
Ying Zhang interconnects Scalability, Graph, Power graph analysis and Pruning in the investigation of issues within Theoretical computer science.
He combines subjects such as Time complexity, Computation, Core and Social network with his study of Vertex.
His Graph research includes elements of Artificial neural network, Reachability, Search engine indexing and Index.
The concepts of his Algorithm study are interwoven with issues in Precision and recall, Nearest neighbor search, Curse of dimensionality and k-nearest neighbors algorithm.
His study looks at the relationship between Nearest neighbor search and topics such as Metric space, which overlap with Data mining.
Between 2019 and 2021, his most popular works were:
- Approximate Nearest Neighbor Search on High Dimensional Data — Experiments, Analyses, and Improvement (63 citations)
- A survey of community search over big graphs (48 citations)
- Efficient Bitruss Decomposition for Large-scale Bipartite Graphs (10 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Machine learning
Graph, Vertex, Combinatorics, Computation and Speedup are his primary areas of study.
His Graph study incorporates themes from Set and Information retrieval, Search engine indexing.
His studies in Set integrate themes in fields like Property and Signed graph.
His study on Vertex also encompasses disciplines like
- Graph which connect with Correctness, Complete bipartite graph, Graph theory, Clique and Theoretical computer science,
- Bipartite graph, which have a strong connection to Process, Enumeration and Enhanced Data Rates for GSM Evolution.
The study incorporates disciplines such as Social network and Vertex in addition to Computation.
His Pruning study combines topics from a wide range of disciplines, such as Range and Data mining.
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