What is he best known for?
The fields of study he is best known for:
- Algorithm
- Artificial intelligence
- Programming language
The scientist’s investigation covers issues in Graph, Theoretical computer science, Relational database, Data mining and SQL.
His Graph research incorporates themes from Time complexity, Connected component, Component and Big data.
The various areas that Lu Qin examines in his Theoretical computer science study include Induced subgraph isomorphism problem, Scalability, Subgraph isomorphism problem, Isomorphism and Vertex.
His work carried out in the field of Vertex brings together such families of science as Complement graph, Null graph, Voltage graph and Pruning.
His work on Relational database management system as part of general Relational database study is frequently linked to Tuple, bridging the gap between disciplines.
His work deals with themes such as Set, Dynamic programming and Search problem, which intersect with Data mining.
His most cited work include:
- Finding Top-k Min-Cost Connected Trees in Databases (276 citations)
- Querying k-truss community in large and dynamic graphs (202 citations)
- Finding time-dependent shortest paths over large graphs (177 citations)
What are the main themes of his work throughout his whole career to date?
Lu Qin focuses on Graph, Theoretical computer science, Vertex, Time complexity and Algorithm.
The concepts of his Graph study are interwoven with issues in Graph and Computation.
His Theoretical computer science research includes themes of Scalability, Data mining, Power graph analysis, Set and Graph bandwidth.
His Graph bandwidth research also works with subjects such as
- Strength of a graph which is related to area like Butterfly graph,
- Complement graph together with Factor-critical graph and Directed graph.
In his study, Bipartite graph and Search engine indexing is inextricably linked to Speedup, which falls within the broad field of Vertex.
His studies deal with areas such as Tree and Connected component as well as Time complexity.
He most often published in these fields:
- Graph (49.20%)
- Theoretical computer science (39.04%)
- Vertex (21.93%)
What were the highlights of his more recent work (between 2019-2021)?
- Graph (49.20%)
- Theoretical computer science (39.04%)
- Vertex (21.93%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Graph, Theoretical computer science, Vertex, Speedup and Computation.
His Graph study combines topics in areas such as Artificial neural network, Algorithm and Search engine indexing.
Lu Qin has researched Theoretical computer science in several fields, including Scalability, Graph, Power graph analysis, Pruning and Matching.
His research investigates the connection between Vertex and topics such as Path that intersect with problems in Topological graph theory and Tree.
His Speedup research is multidisciplinary, incorporating perspectives in Set, Distributed computing, Data structure and Bipartite graph.
His Computation study combines topics from a wide range of disciplines, such as Time complexity, Discrete mathematics, Triangle listing and Efficient algorithm.
Between 2019 and 2021, his most popular works were:
- A survey of community search over big graphs (48 citations)
- Efficient Bitruss Decomposition for Large-scale Bipartite Graphs (10 citations)
- Finding Critical Users in Social Communities: The Collapsed Core and Truss Problems (9 citations)
In his most recent research, the most cited papers focused on:
- Algorithm
- Artificial intelligence
- Programming language
Graph, Theoretical computer science, Vertex, Computation and Bipartite graph are his primary areas of study.
His study on Community search is often connected to Bounded function as part of broader study in Graph.
His study in Theoretical computer science is interdisciplinary in nature, drawing from both Scalability, Artificial neural network, Isomorphism, Node and Complex network.
His biological study spans a wide range of topics, including Graph, Correctness, Complete bipartite graph, Graph theory and Clique.
The study incorporates disciplines such as Contraction hierarchies, Variety and Data structure in addition to Vertex.
His Computation research incorporates elements of Discrete mathematics, Time complexity, Edit distance, Core and Graph similarity.
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