What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
Wenjie Zhang mostly deals with Data mining, Theoretical computer science, Uncertain data, Information retrieval and Search engine indexing.
Wenjie Zhang connects Data mining with Spatial query in his study.
Wenjie Zhang interconnects Euclidean space, Representation, Cluster analysis and Subgraph isomorphism problem, Graph in the investigation of issues within Theoretical computer science.
As part of his studies on Uncertain data, he frequently links adjacent subjects like Probabilistic logic.
His Information retrieval research includes themes of Ranking and Landmark.
His study looks at the relationship between Search engine indexing and topics such as Range query, which overlap with Pruning.
His most cited work include:
- Ranking queries on uncertain data: a probabilistic threshold approach (256 citations)
- Robust Subspace Clustering for Multi-View Data by Exploiting Correlation Consensus (190 citations)
- Effective Multi-Query Expansions: Collaborative Deep Networks for Robust Landmark Retrieval (147 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Theoretical computer science, Data mining, Graph, Vertex and Computation.
His research in Theoretical computer science intersects with topics in Subgraph isomorphism problem, Scalability, Cluster analysis and Power graph analysis.
His work deals with themes such as Object, Probabilistic logic, Search engine indexing and Pruning, which intersect with Data mining.
His Object research is multidisciplinary, relying on both Range and k-nearest neighbors algorithm.
His Graph study integrates concerns from other disciplines, such as Time complexity, Algorithm and Graph.
His studies in Vertex integrate themes in fields like Graph power, Path, Graph database, Level structure and Vertex.
He most often published in these fields:
- Theoretical computer science (32.26%)
- Data mining (29.95%)
- Graph (25.81%)
What were the highlights of his more recent work (between 2019-2021)?
- Graph (25.81%)
- Vertex (17.51%)
- Theoretical computer science (32.26%)
In recent papers he was focusing on the following fields of study:
His main research concerns Graph, Vertex, Theoretical computer science, Computation and Bipartite graph.
His research in the fields of Community search overlaps with other disciplines such as Bounded function, Event, Component and Point.
Wenjie Zhang has researched Vertex in several fields, including Path, Triangle listing and Social network.
His Theoretical computer science study combines topics from a wide range of disciplines, such as Scalability, Graph and Pruning.
His work carried out in the field of Computation brings together such families of science as Discrete mathematics, Time complexity, Core and Vertex.
His Quadtree research is multidisciplinary, incorporating elements of Data mining and Pattern matching.
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)
- Effective and efficient community search over large heterogeneous information networks (18 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Wenjie Zhang mainly focuses on Graph, Computation, Vertex, Theoretical computer science and Community search.
His Graph research is multidisciplinary, incorporating perspectives in Social media, Thesaurus and Data science.
The various areas that Wenjie Zhang examines in his Computation study include Core, Combinatorics and Vertex.
In his study, Algorithm is inextricably linked to Graph, which falls within the broad field of Core.
Wenjie Zhang integrates several fields in his works, including Theoretical computer science and Matrix decomposition.
His research integrates issues of Information retrieval and Knowledge graph in his study of Community search.
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.
