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Laplacian Change Point Detection for Dynamic Graphs:

, In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining,

Huang, Shenyang ; Hitti, Yasmeen ; Rabusseau, Guillaume. - p. 349-358 , 2020

Link: https://dl.acm.org/doi/10.1145/3394486.3403077

RT 
T1 <cTitle>Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining</cTitle>: T1 <Title>Laplacian Change Point Detection for Dynamic Graphs</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3403077&Exemplar=1&LAN=DE
				
A1 Huang, Shenyang
A1 Hitti, Yasmeen
A1 Rabusseau, Guillaume
A1 Rabbany, Reihaneh
PB ACM
YR 2020
K1 anomaly detection
K1 change point detection
K1 dynamic graphs
K1 spectral methods
K1 Computing methodologies
K1 Machine learning
K1 Learning paradigms
K1 Unsupervised learning
K1 Anomaly detection
K1 Theory of computation
K1 Design and analysis of algorithms
K1 Graph algorithms analysis
K1 Dynamic graph algorithms
K1 Mathematics of computing
K1 Discrete mathematics
K1 Graph theory
K1 Spectra of graphs
K1 Artificial intelligence
K1 Knowledge representation and reasoning
K1 Temporal reasoning
K1 Machine learning algorithms
K1 Spectral methods
SP 349
OP 358
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3394486.3403077
DO https://dl.acm.org/doi/10.1145/3394486.3403077
SF ELIB - SuUB Bremen

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