Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Le, Can M."'
Network-linked data, where multivariate observations are interconnected by a network, are becoming increasingly prevalent in fields such as sociology and biology. These data often exhibit inherent noise and complex relational structures, complicating
Externí odkaz:
http://arxiv.org/abs/2410.01163
Autor:
Shao, Zhixuan, Le, Can M.
Community structures represent a crucial aspect of network analysis, and various methods have been developed to identify these communities. However, a common hurdle lies in determining the number of communities K, a parameter that often requires esti
Externí odkaz:
http://arxiv.org/abs/2406.04423
Autor:
Shao, Zhixuan, Le, Can M.
This paper studies the parametric bootstrap method for networks to quantify the uncertainty of statistics of interest. While existing network resampling methods primarily focus on count statistics under node-exchangeable (graphon) models, we consider
Externí odkaz:
http://arxiv.org/abs/2402.01866
Autor:
Li, Xuezhen, Le, Can M.
Variational inference has been widely used in machine learning literature to fit various Bayesian models. In network analysis, this method has been successfully applied to solve the community detection problems. Although these results are promising,
Externí odkaz:
http://arxiv.org/abs/2301.04771
Autor:
Li, Tianxi, Le, Can M.
Networks analysis has been commonly used to study the interactions between units of complex systems. One problem of particular interest is learning the network's underlying connection pattern given a single and noisy instantiation. While many methods
Externí odkaz:
http://arxiv.org/abs/2106.02803
Autor:
Le, Can M., Li, Tianxi
Linear regression on network-linked observations has been an essential tool in modeling the relationship between response and covariates with additional network structures. Previous methods either lack inference tools or rely on restrictive assumptio
Externí odkaz:
http://arxiv.org/abs/2007.00803
Autor:
Li, Tianxi1 (AUTHOR), Le, Can M.2 (AUTHOR) canle@ucdavis.edu
Publikováno v:
Journal of the American Statistical Association. Sep2024, Vol. 119 Issue 547, p2190-2205. 16p.
Random matrix theory has played an important role in recent work on statistical network analysis. In this paper, we review recent results on regimes of concentration of random graphs around their expectation, showing that dense graphs concentrate and
Externí odkaz:
http://arxiv.org/abs/1801.08724
Autor:
Le, Can M.
Edge sampling is an important topic in network analysis. It provides a natural way to reduce network size while retaining desired features of the original network. Sampling methods that only use local information are common in practice as they do not
Externí odkaz:
http://arxiv.org/abs/1710.04772
Complex interactions between entities are often represented as edges in a network. In practice, the network is often constructed from noisy measurements and inevitably contains some errors. In this paper we consider the problem of estimating a networ
Externí odkaz:
http://arxiv.org/abs/1710.04765