What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Gene
His primary areas of investigation include Complex network, Artificial intelligence, Data science, Theoretical computer science and Shape analysis.
Luciano da Fontoura Costa combines Complex network and Set in his studies.
His Artificial intelligence research incorporates themes from Focus, Pattern recognition, Computer vision and Natural language processing.
His Data science research incorporates elements of Citation, Computational biology, Systems biology, Text mining and Network science.
His work deals with themes such as Dynamical systems theory, Club, Tree structure and Calculus, which intersect with Theoretical computer science.
His work carried out in the field of Shape analysis brings together such families of science as Interactive Learning, Image capture and Information retrieval.
His most cited work include:
- Characterization of complex networks: A survey of measurements (1248 citations)
- Shape Analysis and Classification: Theory and Practice (627 citations)
- Analyzing and modeling real-world phenomena with complex networks: a survey of applications (491 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Complex network, Artificial intelligence, Theoretical computer science, Topology and Node.
The Complex network study combines topics in areas such as Identification, Network model, Degree, Network topology and Random walk.
The study incorporates disciplines such as Natural language processing, Computer vision and Pattern recognition in addition to Artificial intelligence.
Luciano da Fontoura Costa specializes in Pattern recognition, namely Principal component analysis.
Theoretical computer science is closely attributed to Betweenness centrality in his study.
His Degree distribution research extends to the thematically linked field of Topology.
He most often published in these fields:
- Complex network (42.23%)
- Artificial intelligence (27.42%)
- Theoretical computer science (14.81%)
What were the highlights of his more recent work (between 2016-2021)?
- Complex network (42.23%)
- Artificial intelligence (27.42%)
- Identification (8.96%)
In recent papers he was focusing on the following fields of study:
Luciano da Fontoura Costa focuses on Complex network, Artificial intelligence, Identification, Topology and Network science.
Luciano da Fontoura Costa integrates Complex network with Set in his research.
His Artificial intelligence research integrates issues from Natural language processing, Machine learning and Pattern recognition.
His Natural language processing research focuses on Narrative and how it relates to Representation.
His Topology study combines topics from a wide range of disciplines, such as Assortativity and Clustering coefficient.
His Network science study combines topics in areas such as Graph, Theoretical computer science, Harmony and Data science.
Between 2016 and 2021, his most popular works were:
- Clustering algorithms: a comparative approach (112 citations)
- Knowledge acquisition: A Complex networks approach (42 citations)
- Patterns of authors contribution in scientific manuscripts (40 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Gene
His primary scientific interests are in Complex network, Artificial intelligence, Network science, Network model and Set.
His research in Complex network intersects with topics in Identification, Similarity, Complex system, Knowledge organization and Topology.
His studies deal with areas such as Machine learning and Pattern recognition as well as Artificial intelligence.
His Network science research includes themes of Centrality, Objective approach, Applied physics, Citation network and Dynamic network analysis.
His study in Network model is interdisciplinary in nature, drawing from both Context, Natural language processing, Structure, Scale and Knowledge acquisition.
His Data science research is multidisciplinary, incorporating elements of Betweenness centrality, Interconnection and Data mining.
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