The scientist’s investigation covers issues in Data science, Social network, Theoretical computer science, Hierarchy and Set. His Data science research incorporates themes from Citation data, Bayesian statistics and Dynamic network analysis. Sune Lehmann combines subjects such as Human dynamics, Social dynamics, Computational sociology and Social group with his study of Social network.
His biological study spans a wide range of topics, including Node, Clique and Flexibility. Among his Hierarchy studies, there is a synthesis of other scientific areas such as Biological network, Hierarchical organization, Node and Structure. His Biological network study integrates concerns from other disciplines, such as Evolving networks, Clique percolation method, Hierarchical control system and Hierarchical network model.
His primary scientific interests are in Social network, Data science, Theoretical computer science, Complex network and Artificial intelligence. His Social network study incorporates themes from Developmental psychology, Homophily, Dynamic network analysis and Interpersonal ties. His Dynamic network analysis study combines topics in areas such as Social media, Social behavior, Communication and Social dynamics.
His research in Data science intersects with topics in Bayesian statistics and Social system. His study explores the link between Theoretical computer science and topics such as Node that cross with problems in Biological network. His Artificial intelligence research includes themes of Machine learning, Metadata and Set.
Sune Lehmann spends much of his time researching Coronavirus disease 2019, Contact tracing, Tracing, Scale and Data science. His Pandemic study in the realm of Coronavirus disease 2019 interacts with subjects such as Risk analysis, Control, Face and Econometrics. His Risk analysis research is multidisciplinary, incorporating elements of Complex system and Robustness.
Sune Lehmann has researched Control in several fields, including Cellular network and Service. His studies deal with areas such as Data collection and Internet privacy as well as Scale. His Data science research is multidisciplinary, incorporating perspectives in Predictability, Citation and Network science, Complex network.
His primary areas of study are Contact tracing, Tracing, Control, Scale and Coronavirus disease 2019. Sune Lehmann regularly ties together related areas like Epidemic control in his Contact tracing studies. His Tracing investigation overlaps with other disciplines such as Distributed computing, Context, Fraction, Artificial intelligence and Machine learning.
Control is frequently linked to Internet privacy in his study. The various areas that Sune Lehmann examines in his Scale study include Information exchange, Location intelligence and Data collection. Coronavirus disease 2019 combines with fields such as University campus, Risk analysis, Stylized fact, Social cost and Reduction in his research.
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Link communities reveal multiscale complexity in networks
Yong-Yeol Ahn;James P. Bagrow;James P. Bagrow;Sune Lehmann;Sune Lehmann.
Understanding the Demographics of Twitter Users
Alan Mislove;Sune Lehmann;Yong-Yeol Ahn;Jukka-Pekka Onnela.
international conference on weblogs and social media (2011)
Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm
Bjarke Felbo;Alan Mislove;Anders Søgaard;Iyad Rahwan.
Measuring large-scale social networks with high resolution.
Arkadiusz Stopczynski;Vedran Sekara;Piotr Sapiezynski;Andrea Cuttone.
PLOS ONE (2014)
Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle.
Nuria Oliver;Bruno Lepri;Harald Sterly;Renaud Lambiotte;Renaud Lambiotte.
Science Advances (2020)
Measures for measures
Sune Lehmann;Andrew D. Jackson;Benny E. Lautrup.
Fundamental structures of dynamic social networks
Vedran Sekara;Arkadiusz Stopczynski;Sune Lehmann;Sune Lehmann.
Proceedings of the National Academy of Sciences of the United States of America (2016)
Evidence of complex contagion of information in social media: An experiment using Twitter bots.
Bjarke Mønsted;Piotr Sapieżyński;Emilio Ferrara;Sune Lehmann.
PLOS ONE (2017)
Citation networks in high energy physics.
S. Lehmann;B.E. Lautrup;A.D. Jackson.
Physical Review E (2003)
Link communities reveal multi-scale complexity in networks
Yong-Yeol Ahn;James P. Bagrow;Sune Lehmann.
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