What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Matthias Dehmer spends much of his time researching Discrete mathematics, Theoretical computer science, Entropy, Combinatorics and Gene regulatory network.
His Discrete mathematics study incorporates themes from Degree, Joint entropy and Rényi entropy.
His Theoretical computer science research integrates issues from Information theory and Biological network.
He interconnects Distance measurement and Graph in the investigation of issues within Entropy.
His Combinatorics research is mostly focused on the topic Line graph.
The study incorporates disciplines such as Inference, Computational biology, Systems biology, Geometric networks and DNA microarray in addition to Gene regulatory network.
His most cited work include:
- A history of graph entropy measures (322 citations)
- Information processing in complex networks: Graph entropy and information functionals (183 citations)
- Knowledge Discovery and interactive Data Mining in Bioinformatics - State-of-the-Art, future challenges and research directions (150 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Discrete mathematics, Combinatorics, Graph, Theoretical computer science and Artificial intelligence.
His Discrete mathematics research includes themes of Rényi entropy and Topology.
His Graph research is multidisciplinary, incorporating elements of Entropy, Polynomial, Eigenvalues and eigenvectors and Bioinformatics.
His work on Graph entropy and Information diagram as part of general Entropy research is often related to Fullerene, thus linking different fields of science.
His research in Theoretical computer science intersects with topics in Graph, Information theory, Graph theory, Inequality and Complex network.
His work deals with themes such as Machine learning and Pattern recognition, which intersect with Artificial intelligence.
He most often published in these fields:
- Discrete mathematics (23.10%)
- Combinatorics (20.69%)
- Graph (16.21%)
What were the highlights of his more recent work (between 2018-2021)?
- Combinatorics (20.69%)
- Artificial intelligence (17.59%)
- Graph (16.21%)
In recent papers he was focusing on the following fields of study:
Combinatorics, Artificial intelligence, Graph, Discrete mathematics and Entropy are his primary areas of study.
His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Pattern recognition.
The various areas that Matthias Dehmer examines in his Graph study include Theoretical computer science and Search engine.
Matthias Dehmer applies his multidisciplinary studies on Theoretical computer science and Transmission in his research.
His biological study spans a wide range of topics, including Measure, Polynomial and Order.
His study in the field of Graph entropy is also linked to topics like Fullerene.
Between 2018 and 2021, his most popular works were:
- A new coupled disease-awareness spreading model with mass media on multiplex networks (77 citations)
- A new coupled disease-awareness spreading model with mass media on multiplex networks (77 citations)
- An Introductory Review of Deep Learning for Prediction Models With Big Data. (31 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Matthias Dehmer mainly investigates Artificial intelligence, Entropy, Graph entropy, Deep learning and Fullerene.
His Artificial intelligence research incorporates elements of Machine learning, Network science and Statistical inference.
Much of his study explores Entropy relationship to Graph.
His Graph entropy research is classified as research in Combinatorics.
His study in Deep learning is interdisciplinary in nature, drawing from both Network architecture, Artificial neural network, Convolutional neural network and Big data.
His work blends Discrete mathematics and Focus studies together.
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