B. Aditya Prakash mainly investigates Graph, Theoretical computer science, Graph theory, Artificial intelligence and Spotting. His Graph research focuses on subjects like Network topology, which are linked to Router. B. Aditya Prakash interconnects Vulnerability, Complex network, Topology and Viral marketing in the investigation of issues within Theoretical computer science.
His Graph theory study deals with Network security intersecting with Robustness. In his research, he performs multidisciplinary study on Artificial intelligence and Product. His study in Spotting is interdisciplinary in nature, drawing from both Analytics and Data science.
The scientist’s investigation covers issues in Theoretical computer science, Artificial intelligence, Graph, Data mining and Machine learning. His studies deal with areas such as Node, Graph theory, Representation and Viral marketing as well as Theoretical computer science. His work in the fields of Artificial intelligence, such as Feature extraction, overlaps with other areas such as Product.
In the subject of general Graph, his work in Power graph analysis is often linked to Term, thereby combining diverse domains of study. His biological study spans a wide range of topics, including Efficient algorithm, Critical infrastructure and Segmentation. As a member of one scientific family, B. Aditya Prakash mostly works in the field of Machine learning, focusing on Social network and, on occasion, Social media.
His primary areas of investigation include Pandemic, Artificial intelligence, Machine learning, Critical infrastructure and Outbreak. He mostly deals with Deep learning in his studies of Artificial intelligence. His Deep learning research is multidisciplinary, relying on both Artificial neural network, Leverage and Noisy data.
His studies in Critical infrastructure integrate themes in fields like Data mining and Greedy algorithm. His work deals with themes such as Ground truth, Segmentation and Robustness, which intersect with Data mining. His Sample research overlaps with Redundancy, Theoretical computer science, Local area network and Minimum description length.
Machine learning, Artificial intelligence, Deep learning, Artificial neural network and Scientific modelling are his primary areas of study. His Machine learning study frequently draws connections to other fields, such as Noisy data. His Artificial neural network study spans across into areas like Outbreak, Pandemic and Flexibility.
His Scientific modelling research incorporates Probabilistic logic, Econometrics, Staffing, Independent research and Geospatial analysis. B. Aditya Prakash undertakes multidisciplinary investigations into Probabilistic logic and High variability in his work.
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.
Rise and fall patterns of information diffusion: model and implications
Yasuko Matsubara;Yasushi Sakurai;B. Aditya Prakash;Lei Li.
knowledge discovery and data mining (2012)
Threshold conditions for arbitrary cascade models on arbitrary networks
B. Aditya Prakash;Deepayan Chakrabarti;Nicholas C. Valler;Michalis Faloutsos.
Knowledge and Information Systems (2012)
Spotting Culprits in Epidemics: How Many and Which Ones?
B. Aditya Prakash;Jilles Vreeken;Christos Faloutsos.
international conference on data mining (2012)
Gelling, and melting, large graphs by edge manipulation
Hanghang Tong;B. Aditya Prakash;Tina Eliassi-Rad;Michalis Faloutsos.
conference on information and knowledge management (2012)
Virus propagation on time-varying networks: theory and immunization algorithms
B. Aditya Prakash;Hanghang Tong;Nicholas Valler;Michalis Faloutsos.
european conference on machine learning (2010)
EigenSpokes: surprising patterns and scalable community chipping in large graphs
B. Aditya Prakash;Ashwin Sridharan;Mukund Seshadri;Sridhar Machiraju.
knowledge discovery and data mining (2010)
On the Vulnerability of Large Graphs
Hanghang Tong;B. Aditya Prakash;Charalampos Tsourakakis;Tina Eliassi-Rad.
international conference on data mining (2010)
Winner takes all: competing viruses or ideas on fair-play networks
B. Aditya Prakash;Alex Beutel;Roni Rosenfeld;Christos Faloutsos.
the web conference (2012)
Interacting viruses in networks: can both survive?
Alex Beutel;B. Aditya Prakash;Roni Rosenfeld;Christos Faloutsos.
knowledge discovery and data mining (2012)
Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S.
Evan L Ray;Nutcha Wattanachit;Jarad Niemi;Abdul Hannan Kanji.
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