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pro vyhledávání: '"Ward, Owen G."'
Embedding the nodes of a large network into an Euclidean space is a common objective in modern machine learning, with a variety of tools available. These embeddings can then be used as features for tasks such as community detection/node clustering or
Externí odkaz:
http://arxiv.org/abs/2310.17712
The mixed membership stochastic blockmodel (MMSB) is a popular Bayesian network model for community detection. Fitting such large Bayesian network models quickly becomes computationally infeasible when the number of nodes grows into hundreds of thous
Externí odkaz:
http://arxiv.org/abs/2108.01727
Group-based social dominance hierarchies are of essential interest in animal behavior research. Studies often record aggressive interactions observed over time, and models that can capture such dynamic hierarchy are therefore crucial. Traditional ran
Externí odkaz:
http://arxiv.org/abs/2012.09598
A common goal in network modeling is to uncover the latent community structure present among nodes. For many real-world networks, the true connections consist of events arriving as streams, which are then aggregated to form edges, ignoring the dynami
Externí odkaz:
http://arxiv.org/abs/2009.01742
Embedding nodes of a large network into a metric (e.g., Euclidean) space has become an area of active research in statistical machine learning, which has found applications in natural and social sciences. Generally, a representation of a network obje
Externí odkaz:
http://arxiv.org/abs/2007.05385
Modeling event dynamics is central to many disciplines. Patterns in observed event arrival times are commonly modeled using point processes. Such event arrival data often exhibits self-exciting, heterogeneous and sporadic trends, which is challenging
Externí odkaz:
http://arxiv.org/abs/1903.03223
Autor:
Ward, Owen G.1 (AUTHOR), Wu, Jing1 (AUTHOR), Zheng, Tian1 (AUTHOR) tzheng@stat.columbia.edu, Smith, Anna L.2 (AUTHOR), Curley, James P.3 (AUTHOR)
Publikováno v:
Journal of the Royal Statistical Society: Series C (Applied Statistics). Nov2022, Vol. 71 Issue 5, p1402-1426. 25p.
Publikováno v:
Statistics & Computing; Feb2024, Vol. 34 Issue 1, p1-28, 28p
Autor:
Ward, Owen G.1 (AUTHOR), Huang, Zhen1 (AUTHOR), Davison, Andrew1 (AUTHOR), Zheng, Tian1,2 (AUTHOR) tian.zheng@columbia.edu
Publikováno v:
Statistical Analysis & Data Mining. Feb2021, Vol. 14 Issue 1, p5-17. 13p.
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