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Predicting Opinion Dynamics via Sociologically-Informed Neu..:

, In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining,

Okawa, Maya ; Iwata, Tomoharu - p. 1306-1316 , 2022

Link: https://dl.acm.org/doi/10.1145/3534678.3539228

RT 
T1 <cTitle>Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining</cTitle>: T1 <Title>Predicting Opinion Dynamics via Sociologically-Informed Neural Networks</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3539228&Exemplar=1&LAN=DE
				
A1 Okawa, Maya
A1 Iwata, Tomoharu
PB ACM
YR 2022
K1 opinion dynamics
K1 sequence prediction
K1 social network
K1 Information systems
K1 Information systems applications
K1 Collaborative and social computing systems and tools
K1 Social networking sites
K1 Mathematics of computing
K1 Mathematical analysis
K1 Differential equations
K1 Ordinary differential equations
K1 Computing methodologies
K1 Machine learning
K1 Machine learning approaches
K1 Neural networks
K1 Probability and statistics
K1 Probabilistic representations
K1 Stochastic differential equations
K1 Applied computing
K1 Law, social and behavioral sciences
K1 Psychology
K1 Sociology
K1 Statistical paradigms
K1 Time series analysis
SP 1306
OP 1316
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3534678.3539228
DO https://dl.acm.org/doi/10.1145/3534678.3539228
SF ELIB - SuUB Bremen

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