What is he best known for?
The fields of study he is best known for:
- Programming language
- Artificial intelligence
- Database
Laks V. S. Lakshmanan mainly focuses on Data mining, Set, Theoretical computer science, Maximization and Mathematical optimization.
His work in the fields of Data mining, such as Query language, intersects with other areas such as Mobile telephony.
His studies deal with areas such as Apriori algorithm and Database theory as well as Set.
His Theoretical computer science research incorporates themes from Data integrity, Cube and Quotient.
Laks V. S. Lakshmanan interconnects Approximation algorithm, Greedy algorithm, Expected value, Heuristics and Approximation theory in the investigation of issues within Maximization.
His studies examine the connections between Relational database and genetics, as well as such issues in Interoperability, with regards to Database.
His most cited work include:
- Learning influence probabilities in social networks (829 citations)
- Exploratory mining and pruning optimizations of constrained associations rules (711 citations)
- CELF++: optimizing the greedy algorithm for influence maximization in social networks (486 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Data mining, Database, Information retrieval, Theoretical computer science and Maximization.
His Data mining research includes themes of Set and Constraint.
His Database research integrates issues from XML validation, XML framework, Efficient XML Interchange and XML database.
His study focuses on the intersection of Information retrieval and fields such as XML with connections in the field of Programming language.
His work deals with themes such as Data modeling, Aggregate, Tree, Online analytical processing and Data integrity, which intersect with Theoretical computer science.
In his work, Scalability is strongly intertwined with Social network, which is a subfield of Maximization.
He most often published in these fields:
- Data mining (21.30%)
- Database (19.57%)
- Information retrieval (16.09%)
What were the highlights of his more recent work (between 2014-2021)?
- Maximization (11.30%)
- Approximation algorithm (9.13%)
- Viral marketing (6.52%)
In recent papers he was focusing on the following fields of study:
Laks V. S. Lakshmanan mainly investigates Maximization, Approximation algorithm, Viral marketing, Graph and Data science.
His Maximization study is related to the wider topic of Mathematical optimization.
His studies in Approximation algorithm integrate themes in fields like Overhead, Computation and Minification.
His Viral marketing study necessitates a more in-depth grasp of Social network.
As part of his studies on Cohesion, Laks V. S. Lakshmanan frequently links adjacent subjects like Data mining.
His study in the fields of Multidimensional data under the domain of Data mining overlaps with other disciplines such as Lossless compression.
Between 2014 and 2021, his most popular works were:
- Attribute-driven community search (95 citations)
- Truss Decomposition of Probabilistic Graphs: Semantics and Algorithms (60 citations)
- Revisiting the stop-and-stare algorithms for influence maximization (57 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Database
Laks V. S. Lakshmanan focuses on Graph, Community search, Maximization, Theoretical computer science and Approximation algorithm.
His study in Community search is interdisciplinary in nature, drawing from both Telecommunications network, Cohesion and Data science.
He combines subjects such as Social media and Viral marketing with his study of Data science.
His work carried out in the field of Maximization brings together such families of science as Social network and Product.
His Theoretical computer science research is multidisciplinary, relying on both Aggregate, Recommender system, Group and Heuristic.
Laks V. S. Lakshmanan has included themes like Analytics, Cluster analysis and Computation in his Approximation algorithm study.
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